Galit Shmueli, David Martens, Jaewon Yoo, Travis Greene
arXiv 19 May 2025 · Statistics — Machine Learning
arXiv:2505.13324 · PDF · DOI · OpenAlex · Extracted main text
Counterfactuals play a pivotal role in the two distinct data science fields of causal inference (CI) and explainable artificial intelligence (XAI). While the core idea behind counterfactuals remains the same in both fields--the examination of what would have happened under different circumstances--there are key differences in how they are used and interpreted. We introduce a formal definition that encompasses the multi-faceted concept of the counterfactual in CI and XAI. We then discuss how counterfactuals are used, evaluated, generated, and operationalized in CI vs. XAI, highlighting conceptual and practical differences. By comparing and contrasting the two, we hope to identify opportunities for cross-fertilization across CI and XAI.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wachter, S., Mittelstadt, B., and Russell, C (2017) Counterfactual explanations without opening the black box: Automated decisions and the gdpr | 0.874 | 5 | 2 | 100% |
| 2 | Xu, K., Chan, J., Ghose, A., and Han, S. P (2017) Battle of the channels: The impact of tablets on digital commerce | 0.737 | 3 | 3 | 67% |
| 3 | Dickerman, B. A. and Hernán, M. A (2020) Counterfactual prediction is not only for causal inference | 0.644 | 2 | 2 | 100% |
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| 7 | Rubin, D. B (1974) Estimating causal effects of treatments in randomized and nonrandomized studies | 0.511 | 2 | 2 | 50% |
| 8 | Tirunillai, S. and Tellis, G. J (2017) Does offline tv advertising affect online chatter? quasi-experimental analysis using synthetic control | 0.511 | 2 | 2 | 50% |
| 9 | Glymour, M., Pearl, J., and Jewell, N. P (2016) Causal inference in statistics: A primer | 0.511 | 2 | 1 | 100% |
| 10 | Guidotti, R (2022) Counterfactual explanations and how to find them: literature review and benchmarking | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 41 scored citations.