Causal Data Integration Article Swipe

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Brit Youngmann , Michael Cafarella , Babak Salimi , Anna Zeng ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2305.08741

Causal inference is fundamental to empirical scientific discoveries in natural and social sciences; however, in the process of conducting causal inference, data management problems can lead to false discoveries. Two such problems are (i) not having all attributes required for analysis, and (ii) misidentifying which attributes are to be included in the analysis. Analysts often only have access to partial data, and they critically rely on (often unavailable or incomplete) domain knowledge to identify attributes to include for analysis, which is often given in the form of a causal DAG. We argue that data management techniques can surmount both of these challenges. In this work, we introduce the Causal Data Integration (CDI) problem, in which unobserved attributes are mined from external sources and a corresponding causal DAG is automatically built. We identify key challenges and research opportunities in designing a CDI system, and present a system architecture for solving the CDI problem. Our preliminary experimental results demonstrate that solving CDI is achievable and pave the way for future research.

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Artificial Intelligence

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Cognition

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Causal inference Computer science Causal model Inference Data science Causal analysis Key (lock) Process (computing) Domain (mathematical analysis) Causal reasoning Management science Risk analysis (engineering) Data mining Artificial intelligence Econometrics Psychology Mathematics Engineering Computer security Statistics Neuroscience Medicine Mathematical analysis Operating system Cognition

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2305.08741
PDF: https://arxiv.org/pdf/2305.08741
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4376877001

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DOI: https://doi.org/10.48550/arxiv.2305.08741

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Title: Causal Data Integration

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-05-15

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Authors: Brit Youngmann, Michael Cafarella, Babak Salimi, Anna Zeng

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Landing page: https://arxiv.org/abs/2305.08741

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Concepts: Causal inference, Computer science, Causal model, Inference, Data science, Causal analysis, Key (lock), Process (computing), Domain (mathematical analysis), Causal reasoning, Management science, Risk analysis (engineering), Data mining, Artificial intelligence, Econometrics, Psychology, Mathematics, Engineering, Computer security, Statistics, Neuroscience, Medicine, Mathematical analysis, Operating system, Cognition

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Cited by: 1

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abstract_inverted_index.inference	1
abstract_inverted_index.introduce	106
abstract_inverted_index.knowledge	71
abstract_inverted_index.research.	168
abstract_inverted_index.sciences;	12
abstract_inverted_index.achievable	161
abstract_inverted_index.attributes	37, 45, 74, 116
abstract_inverted_index.challenges	133
abstract_inverted_index.conducting	18
abstract_inverted_index.critically	63
abstract_inverted_index.inference,	20
abstract_inverted_index.management	22, 94
abstract_inverted_index.scientific	6
abstract_inverted_index.techniques	95
abstract_inverted_index.unobserved	115
abstract_inverted_index.Integration	110
abstract_inverted_index.challenges.	101
abstract_inverted_index.demonstrate	156
abstract_inverted_index.discoveries	7
abstract_inverted_index.fundamental	3
abstract_inverted_index.incomplete)	69
abstract_inverted_index.preliminary	153
abstract_inverted_index.unavailable	67
abstract_inverted_index.architecture	146
abstract_inverted_index.discoveries.	28
abstract_inverted_index.experimental	154
abstract_inverted_index.automatically	128
abstract_inverted_index.corresponding	124
abstract_inverted_index.opportunities	136
abstract_inverted_index.misidentifying	43
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile