Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, Dominik Janzing, David Heckerman
arXiv 12 Apr 2024 · Statistics — Methodology
arXiv:2404.08839 · PDF · DOI · OpenAlex · Extracted main text
Comparing two samples of data, we observe a change in the distribution of an outcome variable. In the presence of multiple explanatory variables, how much of the change can be explained by each possible cause? We develop a new estimation strategy that, given a causal model, combines regression and re-weighting methods to quantify the contribution of each causal mechanism. Our proposed methodology is multiply robust, meaning that it still recovers the target parameter under partial misspecification. We prove that our estimator is consistent and asymptotically normal. Moreover, it can be incorporated into existing frameworks for causal attribution, such as Shapley values, which will inherit the consistency and large-sample distribution properties. Our method demonstrates excellent performance in Monte Carlo simulations, and we show its usefulness in an empirical application. Our method is implemented as part of the Python library DoWhy (arXiv:2011.04216, arXiv:2206.06821).
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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 | Budhathoki, K., Janzing, D., Bloebaum, P., and Ng, H (2021) Why did the distribution change? self | 0.928 | 5 | 4 | 80% |
| 2 | Pearl, J (2009) Causality | 0.843 | 10 | 4 | 60% |
| 3 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 3 | 67% |
| 4 | Chernozhukov, V., Newey, W. K., Quintas-Martínez, V., and Syrgkanis, V (2021) Automatic debiased machine learning via Riesz regression | 0.644 | 2 | 2 | 100% |
| 5 | Daniel, R. M., De Stavola, B. L., Cousens, S. N., and Vansteelandt, S (2015) Causal mediation analysis with multiple mediators | 0.644 | 2 | 2 | 100% |
| 6 | Peters, J., Janzing, D., and Schölkopf, B (2017) Elements of causal inference: foundations and learning algorithms self | 0.644 | 2 | 2 | 100% |
| 7 | Tchetgen-Tchetgen, E. J. and Shpitser, I (2012) Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness, and sensitivity analysis | 0.644 | 2 | 2 | 100% |
| 8 | Belloni, A., Chernozhukov, V., Fernández-Val, I., and Hansen, C (2017) Program evaluation and causal inference with high-dimensional data | 0.511 | 3 | 2 | 33% |
| 9 | Billingsley, P (1995) Probability and Measure | 0.511 | 3 | 2 | 33% |
| 10 | Chernozhukov, V., Newey, W., Singh, R., and Syrgkanis, V (2023) Automatic debiased machine learning for dynamic treatment effects and general nested functionals, 2023 | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 45 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition | 0.644 | 2 | 2 |