Martin Huber, Kevin Kloiber, Lukáš Lafférs
arXiv 4 Mar 2026 · Econometrics
arXiv:2603.04109 · PDF · DOI · OpenAlex · Extracted main text
In causal analysis, understanding the causal mechanisms through which an intervention or treatment affects an outcome is often of central interest. We propose a test to evaluate (i) whether the causal effect of a treatment that is randomly assigned conditional on covariates is fully mediated by, or operates exclusively through, observed intermediate outcomes (referred to as mediators or surrogate outcomes), and (ii) whether the various causal mechanisms operating through different mediators are identifiable conditional on covariates. We demonstrate that if both full mediation and identification of causal mechanisms hold, then the conditionally random treatment is conditionally independent of the outcome given the mediators and covariates. Furthermore, we extend our framework to settings with non-randomly assigned treatments. We show that, in this case, full mediation remains testable, while identification of causal mechanisms is no longer guaranteed. We propose a double machine learning framework for implementing the test that can incorporate high-dimensional covariates and is root-n consistent and asymptotically normal under specific regularity conditions. We also present a simulation study demonstrating good finite-sample performance of our method, along with two empirical applications revisiting randomized experiments on maternal mental health and social norms.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Fulcher, Isabel R. and Shpitser, Ilya and Marealle, Stella and Tchet… (2019) Robust Inference on Population Indirect Causal Effects: The Generalized Front Door Criterion | 1.000 | 5 | 3 | 100% |
| 2 | Baranov, Victoria and Bhalotra, Sonia and Biroli, Pietro and Maselko… (2020) Maternal Depression, Women's Empowerment, and Parental Investment: Evidence from a Randomized Controlled Trial | 1.000 | 5 | 3 | 100% |
| 3 | Bursztyn, Leonardo and Gonzalez, Alessandra L. and Yanagizawa-Drott,… (2020) Misperceived Social Norms: Women Working Outside the Home in Saudi Arabia | 1.000 | 5 | 3 | 100% |
| 4 | Kwon, Soonwoo and Roth, Jonathan (2024) Testing Mechanisms | 0.874 | 11 | 2 | 100% |
| 5 | J. Pearl (2000) Causality: Models, Reasoning, and Inference | 0.874 | 6 | 4 | 67% |
| 6 | Nicolas Apfel and Julia Hatamyar and Martin Huber and Jannis Kueck (2023) Learning control variables and instruments for causal analysis in observational data self | 0.874 | 6 | 2 | 100% |
| 7 | Huber, Martin and Kueck, Jannis (2022) Testing the identification of causal effects in observational data self | 0.843 | 4 | 4 | 75% |
| 8 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.843 | 3 | 3 | 100% |
| 9 | G. W. Imbens (2004) Nonparametric estimation of average treatment effects under exogeneity: a review | 0.843 | 3 | 3 | 100% |
| 10 | J M Robins (2003) Semantics of causal DAG models and the identification of direct and indirect effects | 0.843 | 3 | 3 | 100% |
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