Yang Cai, Alkis Kalavasis, Katerina Mamali, Anay Mehrotra, Manolis Zampetakis
arXiv 4 Jun 2025 · Mathematics — Statistics Theory · 1 citations (OpenAlex)
arXiv:2506.04194 · PDF · DOI · OpenAlex · Extracted main text
Most of the widely used estimators of the average treatment effect (ATE) in causal inference rely on the assumptions of unconfoundedness and overlap. Unconfoundedness requires that the observed covariates account for all correlations between the outcome and treatment. Overlap requires the existence of randomness in treatment decisions for all individuals. Nevertheless, many types of studies frequently violate unconfoundedness or overlap, for instance, observational studies with deterministic treatment decisions - popularly known as Regression Discontinuity designs - violate overlap. In this paper, we initiate the study of general conditions that enable the identification of the average treatment effect, extending beyond unconfoundedness and overlap. In particular, following the paradigm of statistical learning theory, we provide an interpretable condition that is sufficient and necessary for the identification of ATE. Moreover, this condition also characterizes the identification of the average treatment effect on the treated (ATT) and can be used to characterize other treatment effects as well. To illustrate the utility of our condition, we present several well-studied scenarios where our condition is satisfied and, hence, we prove that ATE can be identified in regimes that prior works could not capture. For example, under mild assumptions on the data distributions, this holds for the models proposed by Tan (2006) and Rosenbaum (2002), and the Regression Discontinuity design model introduced by Thistlethwaite and Campbell (1960). For each of these scenarios, we also show that, under natural additional assumptions, ATE can be estimated from finite samples. We believe these findings open new avenues for bridging learning-theoretic insights and causal inference methodologies, particularly in observational studies with complex treatment mechanisms.
appendix boundary found by appendix_command · 62% of the source is main text. Read the extracted text to check this.
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 | Tan, Zhiqiang (2006) A Distributional Approach for Causal Inference Using Propensity Scores | 1.000 | 12 | 4 | 100% |
| 2 | Kalavasis, Alkis, Mehrotra, Anay, Zampetakis, Manolis, Agrawal, Ship… (2024) Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening (Extended Abstract) self | 1.000 | 8 | 3 | 100% |
| 3 | Chernozhukov, Victor, Hansen, Christian, Kallus, Nathan, Spindler, M… (2024) Applied Causal Inference Powered by ML and AI | 1.000 | 7 | 4 | 100% |
| 4 | Li, Fan, Thomas, Laine E., Li, Fan (2018) Addressing Extreme Propensity Scores via the Overlap Weights | 1.000 | 7 | 3 | 100% |
| 5 | Crump, Richard K., Hotz, V. Joseph, Imbens, Guido W., Mitnik, Oscar A (2009) Dealing with Limited Overlap in Estimation of Average Treatment Effects | 1.000 | 6 | 3 | 100% |
| 6 | Khan, Samir, Ugander, Johan (2024) Doubly-Robust and Heteroscedasticity-Aware Sample Trimming for Causal Inference | 1.000 | 5 | 3 | 100% |
| 7 | Rosenbaum, Paul R (2002) Observational Studies | 0.983 | 20 | 6 | 95% |
| 8 | Thistlethwaite, Donald L., Campbell, Donald T (1960) Regression-Discontinuity Analysis: An Alternative to the Ex Post Facto Experiment | 0.961 | 9 | 5 | 89% |
| 9 | Kallus, Nathan, Zhou, Angela (2021) Minimax-Optimal Policy Learning Under Unobserved Confounding | 0.950 | 7 | 5 | 86% |
| 10 | Hahn, Jinyong, Todd, Petra, Klaauw, Wilbert Van (2001) Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design | 0.950 | 7 | 3 | 86% |
Showing the top 10 of 125 scored citations.