Frequent Causal Pattern Mining: A Computationally Efficient Framework For Estimating Bias-Corrected Effects Article Swipe
Pranjul Yadav
,
Michael Steinbach
,
M. Regina Castro
,
Pedro J. Caraballo
,
Vipin Kumar
,
György Simon
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/bigdata47090.2019.9005977
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/bigdata47090.2019.9005977
Our aging population increasingly suffers from multiple chronic diseases simultaneously, necessitating the comprehensive treatment of these conditions. Finding the optimal set of drugs for a combinatorial set of diseases is a combinatorial pattern exploration problem. Association rule mining is a popular tool for such problems, but the requirement of health care for finding causal, rather than associative, patterns renders association rule mining unsuitable. To address this issue, we propose a novel framework based on the Rubin-Neyman causal model for extracting causal rules from observational data, correcting for a number of common biases. Specifically, given a set of interventions and a set of items that define subpopulations (e.g., diseases), we wish to find all subpopulations in which effective intervention combinations exist and in each such subpopulation, we wish to find all intervention combinations such that dropping any intervention from this combination will reduce the efficacy of the treatment. A key aspect of our framework is the concept of closed intervention sets which extend the concept of quantifying the effect of a single intervention to a set of concurrent interventions. Closed intervention sets also allow for a pruning strategy that is strictly more efficient than the traditional pruning strategy used by the Apriori algorithm. To implement our ideas, we introduce and compare five methods of estimating causal effect from observational data and rigorously evaluate them on synthetic data to mathematically prove (when possible) why they work. We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of 152000 patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).
Related Topics
Concepts
Pruning
Observational study
Set (abstract data type)
Computer science
Intervention (counseling)
Association rule learning
A priori and a posteriori
Data mining
Machine learning
Synthetic data
Psychological intervention
Population
Key (lock)
Artificial intelligence
Medicine
Mathematics
Statistics
Biology
Programming language
Philosophy
Epistemology
Environmental health
Psychiatry
Computer security
Agronomy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/bigdata47090.2019.9005977
- OA Status
- green
- Cited By
- 5
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3008484972
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3008484972Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/bigdata47090.2019.9005977Digital Object Identifier
- Title
-
Frequent Causal Pattern Mining: A Computationally Efficient Framework For Estimating Bias-Corrected EffectsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-01Full publication date if available
- Authors
-
Pranjul Yadav, Michael Steinbach, M. Regina Castro, Pedro J. Caraballo, Vipin Kumar, György SimonList of authors in order
- Landing page
-
https://doi.org/10.1109/bigdata47090.2019.9005977Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/7730315Direct OA link when available
- Concepts
-
Pruning, Observational study, Set (abstract data type), Computer science, Intervention (counseling), Association rule learning, A priori and a posteriori, Data mining, Machine learning, Synthetic data, Psychological intervention, Population, Key (lock), Artificial intelligence, Medicine, Mathematics, Statistics, Biology, Programming language, Philosophy, Epistemology, Environmental health, Psychiatry, Computer security, AgronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3008484972 |
|---|---|
| doi | https://doi.org/10.1109/bigdata47090.2019.9005977 |
| ids.doi | https://doi.org/10.1109/bigdata47090.2019.9005977 |
| ids.mag | 3008484972 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/33313606 |
| ids.openalex | https://openalex.org/W3008484972 |
| fwci | 1.09203011 |
| type | article |
| title | Frequent Causal Pattern Mining: A Computationally Efficient Framework For Estimating Bias-Corrected Effects |
| biblio.issue | |
| biblio.volume | 7 |
| biblio.last_page | 1990 |
| biblio.first_page | 1981 |
| topics[0].id | https://openalex.org/T10538 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9803000092506409 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1710 |
| topics[0].subfield.display_name | Information Systems |
| topics[0].display_name | Data Mining Algorithms and Applications |
| topics[1].id | https://openalex.org/T10845 |
| topics[1].field.id | https://openalex.org/fields/26 |
| topics[1].field.display_name | Mathematics |
| topics[1].score | 0.9697999954223633 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2613 |
| topics[1].subfield.display_name | Statistics and Probability |
| topics[1].display_name | Advanced Causal Inference Techniques |
| topics[2].id | https://openalex.org/T10394 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9667999744415283 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2746 |
| topics[2].subfield.display_name | Surgery |
| topics[2].display_name | Lipoproteins and Cardiovascular Health |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C108010975 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7309408187866211 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q500094 |
| concepts[0].display_name | Pruning |
| concepts[1].id | https://openalex.org/C23131810 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6142755746841431 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q818574 |
| concepts[1].display_name | Observational study |
| concepts[2].id | https://openalex.org/C177264268 |
| concepts[2].level | 2 |
| concepts[2].score | 0.596118152141571 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[2].display_name | Set (abstract data type) |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.5877254605293274 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C2780665704 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5639305710792542 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q959298 |
| concepts[4].display_name | Intervention (counseling) |
| concepts[5].id | https://openalex.org/C193524817 |
| concepts[5].level | 2 |
| concepts[5].score | 0.561176061630249 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q386780 |
| concepts[5].display_name | Association rule learning |
| concepts[6].id | https://openalex.org/C75553542 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5056777000427246 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q178161 |
| concepts[6].display_name | A priori and a posteriori |
| concepts[7].id | https://openalex.org/C124101348 |
| concepts[7].level | 1 |
| concepts[7].score | 0.49537813663482666 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[7].display_name | Data mining |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4761376678943634 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C160920958 |
| concepts[9].level | 2 |
| concepts[9].score | 0.44791659712791443 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7662746 |
| concepts[9].display_name | Synthetic data |
| concepts[10].id | https://openalex.org/C27415008 |
| concepts[10].level | 2 |
| concepts[10].score | 0.43870651721954346 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7256382 |
| concepts[10].display_name | Psychological intervention |
| concepts[11].id | https://openalex.org/C2908647359 |
| concepts[11].level | 2 |
| concepts[11].score | 0.43403488397598267 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2625603 |
| concepts[11].display_name | Population |
| concepts[12].id | https://openalex.org/C26517878 |
| concepts[12].level | 2 |
| concepts[12].score | 0.4177170395851135 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q228039 |
| concepts[12].display_name | Key (lock) |
| concepts[13].id | https://openalex.org/C154945302 |
| concepts[13].level | 1 |
| concepts[13].score | 0.37502068281173706 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[13].display_name | Artificial intelligence |
| concepts[14].id | https://openalex.org/C71924100 |
| concepts[14].level | 0 |
| concepts[14].score | 0.2539641857147217 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[14].display_name | Medicine |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.23396846652030945 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C105795698 |
| concepts[16].level | 1 |
| concepts[16].score | 0.15813246369361877 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[16].display_name | Statistics |
| concepts[17].id | https://openalex.org/C86803240 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[17].display_name | Biology |
| concepts[18].id | https://openalex.org/C199360897 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[18].display_name | Programming language |
| concepts[19].id | https://openalex.org/C138885662 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[19].display_name | Philosophy |
| concepts[20].id | https://openalex.org/C111472728 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q9471 |
| concepts[20].display_name | Epistemology |
| concepts[21].id | https://openalex.org/C99454951 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q932068 |
| concepts[21].display_name | Environmental health |
| concepts[22].id | https://openalex.org/C118552586 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q7867 |
| concepts[22].display_name | Psychiatry |
| concepts[23].id | https://openalex.org/C38652104 |
| concepts[23].level | 1 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[23].display_name | Computer security |
| concepts[24].id | https://openalex.org/C6557445 |
| concepts[24].level | 1 |
| concepts[24].score | 0.0 |
| concepts[24].wikidata | https://www.wikidata.org/wiki/Q173113 |
| concepts[24].display_name | Agronomy |
| keywords[0].id | https://openalex.org/keywords/pruning |
| keywords[0].score | 0.7309408187866211 |
| keywords[0].display_name | Pruning |
| keywords[1].id | https://openalex.org/keywords/observational-study |
| keywords[1].score | 0.6142755746841431 |
| keywords[1].display_name | Observational study |
| keywords[2].id | https://openalex.org/keywords/set |
| keywords[2].score | 0.596118152141571 |
| keywords[2].display_name | Set (abstract data type) |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.5877254605293274 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/intervention |
| keywords[4].score | 0.5639305710792542 |
| keywords[4].display_name | Intervention (counseling) |
| keywords[5].id | https://openalex.org/keywords/association-rule-learning |
| keywords[5].score | 0.561176061630249 |
| keywords[5].display_name | Association rule learning |
| keywords[6].id | https://openalex.org/keywords/a-priori-and-a-posteriori |
| keywords[6].score | 0.5056777000427246 |
| keywords[6].display_name | A priori and a posteriori |
| keywords[7].id | https://openalex.org/keywords/data-mining |
| keywords[7].score | 0.49537813663482666 |
| keywords[7].display_name | Data mining |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.4761376678943634 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/synthetic-data |
| keywords[9].score | 0.44791659712791443 |
| keywords[9].display_name | Synthetic data |
| keywords[10].id | https://openalex.org/keywords/psychological-intervention |
| keywords[10].score | 0.43870651721954346 |
| keywords[10].display_name | Psychological intervention |
| keywords[11].id | https://openalex.org/keywords/population |
| keywords[11].score | 0.43403488397598267 |
| keywords[11].display_name | Population |
| keywords[12].id | https://openalex.org/keywords/key |
| keywords[12].score | 0.4177170395851135 |
| keywords[12].display_name | Key (lock) |
| keywords[13].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[13].score | 0.37502068281173706 |
| keywords[13].display_name | Artificial intelligence |
| keywords[14].id | https://openalex.org/keywords/medicine |
| keywords[14].score | 0.2539641857147217 |
| keywords[14].display_name | Medicine |
| keywords[15].id | https://openalex.org/keywords/mathematics |
| keywords[15].score | 0.23396846652030945 |
| keywords[15].display_name | Mathematics |
| keywords[16].id | https://openalex.org/keywords/statistics |
| keywords[16].score | 0.15813246369361877 |
| keywords[16].display_name | Statistics |
| language | en |
| locations[0].id | doi:10.1109/bigdata47090.2019.9005977 |
| locations[0].is_oa | False |
| locations[0].source | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | 2019 IEEE International Conference on Big Data (Big Data) |
| locations[0].landing_page_url | https://doi.org/10.1109/bigdata47090.2019.9005977 |
| locations[1].id | pmid:33313606 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/33313606 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:7730315 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Proc IEEE Int Conf Big Data |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7730315 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5003976436 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-7860-5830 |
| authorships[0].author.display_name | Pranjul Yadav |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I2800403580 |
| authorships[0].affiliations[0].raw_affiliation_string | University of Minnesota |
| authorships[0].institutions[0].id | https://openalex.org/I2800403580 |
| authorships[0].institutions[0].ror | https://ror.org/03grvy078 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I2800403580 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of Minnesota System |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Pranjul Yadav |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | University of Minnesota |
| authorships[1].author.id | https://openalex.org/A5089436894 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7309-6395 |
| authorships[1].author.display_name | Michael Steinbach |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I2800403580 |
| authorships[1].affiliations[0].raw_affiliation_string | University of Minnesota |
| authorships[1].institutions[0].id | https://openalex.org/I2800403580 |
| authorships[1].institutions[0].ror | https://ror.org/03grvy078 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I2800403580 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of Minnesota System |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Michael Steinbach |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | University of Minnesota |
| authorships[2].author.id | https://openalex.org/A5011038225 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2784-8776 |
| authorships[2].author.display_name | M. Regina Castro |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I2802423016 |
| authorships[2].affiliations[0].raw_affiliation_string | Mayo Clinic |
| authorships[2].institutions[0].id | https://openalex.org/I2802423016 |
| authorships[2].institutions[0].ror | https://ror.org/02s47w807 |
| authorships[2].institutions[0].type | nonprofit |
| authorships[2].institutions[0].lineage | https://openalex.org/I2802423016 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | WinnMed |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | M. Regina Castro |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Mayo Clinic |
| authorships[3].author.id | https://openalex.org/A5045680515 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9050-7080 |
| authorships[3].author.display_name | Pedro J. Caraballo |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I2802423016 |
| authorships[3].affiliations[0].raw_affiliation_string | Mayo Clinic |
| authorships[3].institutions[0].id | https://openalex.org/I2802423016 |
| authorships[3].institutions[0].ror | https://ror.org/02s47w807 |
| authorships[3].institutions[0].type | nonprofit |
| authorships[3].institutions[0].lineage | https://openalex.org/I2802423016 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | WinnMed |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Pedro J. Caraballo |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Mayo Clinic |
| authorships[4].author.id | https://openalex.org/A5101672986 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-2511-2546 |
| authorships[4].author.display_name | Vipin Kumar |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I2800403580 |
| authorships[4].affiliations[0].raw_affiliation_string | University of Minnesota |
| authorships[4].institutions[0].id | https://openalex.org/I2800403580 |
| authorships[4].institutions[0].ror | https://ror.org/03grvy078 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I2800403580 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | University of Minnesota System |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Vipin Kumar |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | University of Minnesota |
| authorships[5].author.id | https://openalex.org/A5050008389 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-4715-5934 |
| authorships[5].author.display_name | György Simon |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I2800403580 |
| authorships[5].affiliations[0].raw_affiliation_string | University of Minnesota |
| authorships[5].institutions[0].id | https://openalex.org/I2800403580 |
| authorships[5].institutions[0].ror | https://ror.org/03grvy078 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I2800403580 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | University of Minnesota System |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Gyorgy Simon |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | University of Minnesota |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7730315 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Frequent Causal Pattern Mining: A Computationally Efficient Framework For Estimating Bias-Corrected Effects |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10538 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9803000092506409 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1710 |
| primary_topic.subfield.display_name | Information Systems |
| primary_topic.display_name | Data Mining Algorithms and Applications |
| related_works | https://openalex.org/W4381328000, https://openalex.org/W2109147503, https://openalex.org/W1984720268, https://openalex.org/W2103779230, https://openalex.org/W2322581019, https://openalex.org/W334096847, https://openalex.org/W3141050733, https://openalex.org/W2030936866, https://openalex.org/W1533309927, https://openalex.org/W1688175311 |
| cited_by_count | 5 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2022 |
| counts_by_year[2].cited_by_count | 3 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:7730315 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764455111 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | PubMed Central |
| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | Proc IEEE Int Conf Big Data |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7730315 |
| primary_location.id | doi:10.1109/bigdata47090.2019.9005977 |
| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | 2019 IEEE International Conference on Big Data (Big Data) |
| primary_location.landing_page_url | https://doi.org/10.1109/bigdata47090.2019.9005977 |
| publication_date | 2019-12-01 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W6638571763, https://openalex.org/W1978108654, https://openalex.org/W6639748940, https://openalex.org/W2518113215, https://openalex.org/W6785388961, https://openalex.org/W2256584223, https://openalex.org/W2115482638, https://openalex.org/W1990800901, https://openalex.org/W2116958223, https://openalex.org/W2009187570, https://openalex.org/W2782502282, https://openalex.org/W2572656025, https://openalex.org/W6726935153, https://openalex.org/W2243040572, https://openalex.org/W2179859124, https://openalex.org/W6636824657, https://openalex.org/W1596022446, https://openalex.org/W1992862710, https://openalex.org/W6725108886, https://openalex.org/W2012639032, https://openalex.org/W6628750762, https://openalex.org/W171045336, https://openalex.org/W6638281896, https://openalex.org/W1985252547, https://openalex.org/W2783812063, https://openalex.org/W4242484299, https://openalex.org/W2594685110, https://openalex.org/W2129378933, https://openalex.org/W1961009203, https://openalex.org/W2036193982, https://openalex.org/W6638821019, https://openalex.org/W2150291618, https://openalex.org/W2168639902, https://openalex.org/W2121878111, https://openalex.org/W2611939915, https://openalex.org/W3098060673, https://openalex.org/W2749624884, https://openalex.org/W1641347315, https://openalex.org/W1784684835, https://openalex.org/W1901355268, https://openalex.org/W1824634186, https://openalex.org/W4239098705, https://openalex.org/W2963781702, https://openalex.org/W2949692233, https://openalex.org/W1484413656, https://openalex.org/W2507814050, https://openalex.org/W1990966354, https://openalex.org/W1845768777 |
| referenced_works_count | 48 |
| abstract_inverted_index.A | 147 |
| abstract_inverted_index.a | 24, 30, 39, 69, 87, 94, 99, 169, 173, 184, 250, 282 |
| abstract_inverted_index.To | 63, 202 |
| abstract_inverted_index.We | 234 |
| abstract_inverted_index.by | 198 |
| abstract_inverted_index.in | 114, 121, 274 |
| abstract_inverted_index.is | 29, 38, 153, 188 |
| abstract_inverted_index.of | 14, 21, 27, 48, 89, 96, 101, 144, 150, 156, 164, 168, 175, 212, 249, 253, 281, 284 |
| abstract_inverted_index.on | 73, 223, 242, 287 |
| abstract_inverted_index.to | 110, 127, 172, 226, 269 |
| abstract_inverted_index.we | 67, 108, 125, 206, 264 |
| abstract_inverted_index.Our | 0 |
| abstract_inverted_index.all | 112, 129 |
| abstract_inverted_index.and | 98, 120, 208, 219, 259 |
| abstract_inverted_index.any | 135 |
| abstract_inverted_index.are | 266 |
| abstract_inverted_index.but | 45 |
| abstract_inverted_index.for | 23, 42, 51, 78, 86, 183 |
| abstract_inverted_index.key | 148 |
| abstract_inverted_index.our | 151, 204, 237 |
| abstract_inverted_index.set | 20, 26, 95, 100, 174 |
| abstract_inverted_index.the | 11, 18, 46, 74, 142, 145, 154, 162, 166, 193, 199, 243, 262, 271, 275, 279 |
| abstract_inverted_index.why | 231 |
| abstract_inverted_index.Mayo | 257 |
| abstract_inverted_index.also | 181, 235 |
| abstract_inverted_index.care | 50 |
| abstract_inverted_index.data | 218, 225, 248 |
| abstract_inverted_index.each | 122 |
| abstract_inverted_index.find | 111, 128 |
| abstract_inverted_index.five | 210 |
| abstract_inverted_index.from | 5, 82, 137, 216, 256 |
| abstract_inverted_index.more | 190 |
| abstract_inverted_index.rich | 268 |
| abstract_inverted_index.rule | 36, 60, 239 |
| abstract_inverted_index.sets | 159, 180 |
| abstract_inverted_index.such | 43, 123, 132 |
| abstract_inverted_index.than | 55, 192 |
| abstract_inverted_index.that | 103, 133, 187, 261 |
| abstract_inverted_index.them | 222 |
| abstract_inverted_index.they | 232 |
| abstract_inverted_index.this | 65, 138 |
| abstract_inverted_index.tool | 41 |
| abstract_inverted_index.used | 197 |
| abstract_inverted_index.will | 140 |
| abstract_inverted_index.wish | 109, 126 |
| abstract_inverted_index.(EHR) | 247 |
| abstract_inverted_index.(when | 229 |
| abstract_inverted_index.aging | 1 |
| abstract_inverted_index.allow | 182 |
| abstract_inverted_index.based | 72 |
| abstract_inverted_index.class | 283 |
| abstract_inverted_index.data, | 84 |
| abstract_inverted_index.drugs | 22, 286 |
| abstract_inverted_index.exist | 119 |
| abstract_inverted_index.given | 93 |
| abstract_inverted_index.items | 102 |
| abstract_inverted_index.large | 251 |
| abstract_inverted_index.model | 77 |
| abstract_inverted_index.novel | 70 |
| abstract_inverted_index.prove | 228 |
| abstract_inverted_index.rules | 81 |
| abstract_inverted_index.these | 15 |
| abstract_inverted_index.which | 115, 160 |
| abstract_inverted_index.work. | 233 |
| abstract_inverted_index.(e.g., | 106 |
| abstract_inverted_index.152000 | 254 |
| abstract_inverted_index.Clinic | 258 |
| abstract_inverted_index.Closed | 178 |
| abstract_inverted_index.Health | 245 |
| abstract_inverted_index.aspect | 149 |
| abstract_inverted_index.causal | 76, 80, 214, 238 |
| abstract_inverted_index.closed | 157 |
| abstract_inverted_index.cohort | 252 |
| abstract_inverted_index.common | 90 |
| abstract_inverted_index.define | 104 |
| abstract_inverted_index.effect | 167, 215, 280 |
| abstract_inverted_index.extend | 161 |
| abstract_inverted_index.health | 49 |
| abstract_inverted_index.ideas, | 205 |
| abstract_inverted_index.issue, | 66 |
| abstract_inverted_index.mining | 37, 61, 240 |
| abstract_inverted_index.number | 88 |
| abstract_inverted_index.rather | 54 |
| abstract_inverted_index.reduce | 141 |
| abstract_inverted_index.showed | 260 |
| abstract_inverted_index.single | 170 |
| abstract_inverted_index.(T2DM). | 291 |
| abstract_inverted_index.Apriori | 200 |
| abstract_inverted_index.Finding | 17 |
| abstract_inverted_index.Records | 246 |
| abstract_inverted_index.Type-II | 288 |
| abstract_inverted_index.address | 64 |
| abstract_inverted_index.biases. | 91 |
| abstract_inverted_index.causal, | 53 |
| abstract_inverted_index.chronic | 7 |
| abstract_inverted_index.compare | 209 |
| abstract_inverted_index.concept | 155, 163 |
| abstract_inverted_index.explain | 270 |
| abstract_inverted_index.finding | 52 |
| abstract_inverted_index.medical | 276 |
| abstract_inverted_index.methods | 211 |
| abstract_inverted_index.optimal | 19 |
| abstract_inverted_index.pattern | 32 |
| abstract_inverted_index.popular | 40 |
| abstract_inverted_index.propose | 68 |
| abstract_inverted_index.pruning | 185, 195 |
| abstract_inverted_index.renders | 58 |
| abstract_inverted_index.suffers | 4 |
| abstract_inverted_index.Diabetes | 289 |
| abstract_inverted_index.Mellitus | 290 |
| abstract_inverted_index.diseases | 8, 28 |
| abstract_inverted_index.dropping | 134 |
| abstract_inverted_index.efficacy | 143 |
| abstract_inverted_index.evaluate | 221 |
| abstract_inverted_index.findings | 273 |
| abstract_inverted_index.multiple | 6 |
| abstract_inverted_index.patients | 255 |
| abstract_inverted_index.patterns | 57, 263 |
| abstract_inverted_index.problem. | 34 |
| abstract_inverted_index.strategy | 186, 196 |
| abstract_inverted_index.strictly | 189 |
| abstract_inverted_index.effective | 116 |
| abstract_inverted_index.efficient | 191 |
| abstract_inverted_index.evaluated | 236 |
| abstract_inverted_index.extracted | 265 |
| abstract_inverted_index.framework | 71, 152, 241 |
| abstract_inverted_index.implement | 203 |
| abstract_inverted_index.introduce | 207 |
| abstract_inverted_index.possible) | 230 |
| abstract_inverted_index.problems, | 44 |
| abstract_inverted_index.regarding | 278 |
| abstract_inverted_index.synthetic | 224 |
| abstract_inverted_index.treatment | 13 |
| abstract_inverted_index.Electronic | 244 |
| abstract_inverted_index.algorithm. | 201 |
| abstract_inverted_index.concurrent | 176 |
| abstract_inverted_index.correcting | 85 |
| abstract_inverted_index.diseases), | 107 |
| abstract_inverted_index.estimating | 213 |
| abstract_inverted_index.extracting | 79 |
| abstract_inverted_index.literature | 277 |
| abstract_inverted_index.population | 2 |
| abstract_inverted_index.rigorously | 220 |
| abstract_inverted_index.treatment. | 146 |
| abstract_inverted_index.Association | 35 |
| abstract_inverted_index.association | 59 |
| abstract_inverted_index.cholesterol | 285 |
| abstract_inverted_index.combination | 139 |
| abstract_inverted_index.conditions. | 16 |
| abstract_inverted_index.exploration | 33 |
| abstract_inverted_index.quantifying | 165 |
| abstract_inverted_index.requirement | 47 |
| abstract_inverted_index.traditional | 194 |
| abstract_inverted_index.unsuitable. | 62 |
| abstract_inverted_index.Rubin-Neyman | 75 |
| abstract_inverted_index.associative, | 56 |
| abstract_inverted_index.combinations | 118, 131 |
| abstract_inverted_index.increasingly | 3 |
| abstract_inverted_index.intervention | 117, 130, 136, 158, 171, 179 |
| abstract_inverted_index.sufficiently | 267 |
| abstract_inverted_index.Specifically, | 92 |
| abstract_inverted_index.combinatorial | 25, 31 |
| abstract_inverted_index.comprehensive | 12 |
| abstract_inverted_index.controversial | 272 |
| abstract_inverted_index.interventions | 97 |
| abstract_inverted_index.necessitating | 10 |
| abstract_inverted_index.observational | 83, 217 |
| abstract_inverted_index.interventions. | 177 |
| abstract_inverted_index.mathematically | 227 |
| abstract_inverted_index.subpopulation, | 124 |
| abstract_inverted_index.subpopulations | 105, 113 |
| abstract_inverted_index.simultaneously, | 9 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.84424706 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |