Conformal Counterfactual Inference under Hidden Confounding Article Swipe
Zonghao Chen
,
Ruocheng Guo
,
Jean-François Ton
,
Yang Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3637528.3671976
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3637528.3671976
Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reliability in high-stakes scenarios. Predicting potential outcomes along with its uncertainty in a counterfactual world poses the foundamental challenge in causal inference. Existing methods that construct confidence intervals for counterfactuals either rely on the assumption of strong ignorability that completely ignores hidden confounders, or need access to un-identifiable lower and upper bounds that characterize the difference between observational and interventional distributions. In this paper, to overcome these limitations, we first propose a novel approach wTCP-DR based on transductive weighted conformal prediction, which provides confidence intervals for counterfactual outcomes with marginal converage guarantees, even under hidden confounding. With less restrictive assumptions, our approach requires access to a fraction of interventional data (from randomized controlled trials) to account for the covariate shift from observational distributoin to interventional distribution. Theoretical results explicitly demonstrate the conditions under which our algorithm is strictly advantageous to the naive method that only uses interventional data. Since transductive conformal prediction is notoriously costly, we propose wSCP-DR, a two-stage variant of wTCP-DR, based on split conformal prediction with same marginal coverage guarantees but at a significantly lower computational cost. After ensuring valid intervals on counterfactuals, it is straightforward to construct intervals for individual treatment effects (ITEs). We demonstrate our method across synthetic and real-world data, including recommendation systems, to verify the superiority of our methods compared against state-of-the-art baselines in terms of both coverage and efficiency. Our code can be found at https://github.com/rguo12/KDD24-Conformal.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3637528.3671976
- https://dl.acm.org/doi/pdf/10.1145/3637528.3671976
- OA Status
- gold
- Cited By
- 1
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401863895
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Raw OpenAlex JSON
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https://openalex.org/W4401863895Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3637528.3671976Digital Object Identifier
- Title
-
Conformal Counterfactual Inference under Hidden ConfoundingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-08-24Full publication date if available
- Authors
-
Zonghao Chen, Ruocheng Guo, Jean-François Ton, Yang LiuList of authors in order
- Landing page
-
https://doi.org/10.1145/3637528.3671976Publisher landing page
- PDF URL
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https://dl.acm.org/doi/pdf/10.1145/3637528.3671976Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3637528.3671976Direct OA link when available
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Counterfactual thinking, Confounding, Inference, Computer science, Conformal map, Artificial intelligence, Econometrics, Statistics, Mathematics, Psychology, Social psychology, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.construct | 52, 217 |
| abstract_inverted_index.converage | 117 |
| abstract_inverted_index.covariate | 145 |
| abstract_inverted_index.different | 10 |
| abstract_inverted_index.including | 233 |
| abstract_inverted_index.intervals | 14, 54, 111, 210, 218 |
| abstract_inverted_index.knowledge | 5 |
| abstract_inverted_index.potential | 7, 17, 32 |
| abstract_inverted_index.synthetic | 229 |
| abstract_inverted_index.treatment | 221 |
| abstract_inverted_index.two-stage | 186 |
| abstract_inverted_index.Predicting | 31 |
| abstract_inverted_index.assumption | 61 |
| abstract_inverted_index.completely | 66 |
| abstract_inverted_index.conditions | 158 |
| abstract_inverted_index.confidence | 13, 53, 110 |
| abstract_inverted_index.controlled | 139 |
| abstract_inverted_index.difference | 82 |
| abstract_inverted_index.explicitly | 155 |
| abstract_inverted_index.guarantees | 199 |
| abstract_inverted_index.individual | 220 |
| abstract_inverted_index.inference. | 48 |
| abstract_inverted_index.prediction | 178, 194 |
| abstract_inverted_index.randomized | 138 |
| abstract_inverted_index.real-world | 231 |
| abstract_inverted_index.scenarios. | 30 |
| abstract_inverted_index.Theoretical | 153 |
| abstract_inverted_index.demonstrate | 156, 225 |
| abstract_inverted_index.efficiency. | 253 |
| abstract_inverted_index.guarantees, | 118 |
| abstract_inverted_index.high-stakes | 29 |
| abstract_inverted_index.notoriously | 180 |
| abstract_inverted_index.prediction, | 107 |
| abstract_inverted_index.reliability | 27 |
| abstract_inverted_index.restrictive | 125 |
| abstract_inverted_index.superiority | 239 |
| abstract_inverted_index.treatments, | 11 |
| abstract_inverted_index.uncertainty | 37 |
| abstract_inverted_index.Personalized | 0 |
| abstract_inverted_index.advantageous | 165 |
| abstract_inverted_index.assumptions, | 126 |
| abstract_inverted_index.characterize | 80 |
| abstract_inverted_index.confounders, | 69 |
| abstract_inverted_index.confounding. | 122 |
| abstract_inverted_index.distributoin | 149 |
| abstract_inverted_index.foundamental | 44 |
| abstract_inverted_index.ignorability | 64 |
| abstract_inverted_index.limitations, | 94 |
| abstract_inverted_index.transductive | 104, 176 |
| abstract_inverted_index.computational | 205 |
| abstract_inverted_index.distribution. | 152 |
| abstract_inverted_index.observational | 84, 148 |
| abstract_inverted_index.significantly | 203 |
| abstract_inverted_index.counterfactual | 40, 113 |
| abstract_inverted_index.distributions. | 87 |
| abstract_inverted_index.interventional | 86, 135, 151, 173 |
| abstract_inverted_index.recommendation | 234 |
| abstract_inverted_index.counterfactuals | 56 |
| abstract_inverted_index.decision-making | 22 |
| abstract_inverted_index.straightforward | 215 |
| abstract_inverted_index.un-identifiable | 74 |
| abstract_inverted_index.counterfactuals, | 212 |
| abstract_inverted_index.state-of-the-art | 245 |
| abstract_inverted_index.https://github.com/rguo12/KDD24-Conformal. | 260 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.72143923 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |