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Multiply-Robust Causal Change Attribution (2404.08839v4)

Published 12 Apr 2024 in stat.ME, cs.LG, econ.EM, and stat.ML

Abstract: 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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References (43)
  1. Permutation weighting. In International Conference on Machine Learning, pp. 331–341. PMLR, 2021.
  2. Program evaluation and causal inference with high-dimensional data. Econometrica, 85(1):233–298, 2017.
  3. Berman, R. Beyond the last touch: Attribution in online advertising. Marketing Science, 37(5):771–792, 2018.
  4. Discriminative learning under covariate shift. Journal of Machine Learning Research, 10(9), 2009.
  5. Billingsley, P. Probability and Measure. John Wiley & Sons, third edition, 1995.
  6. Blinder, A. S. Wage discrimination: reduced form and structural estimates. Journal of Human resources, pp.  436–455, 1973.
  7. Why did the distribution change? In AISTATS, 2021.
  8. Causal structure based root cause analysis of outliers. In International Conference on Machine Learning, pp. 2357–2369. PMLR, 2022.
  9. Algorithms to estimate shapley value feature attributions. Nature Machine Intelligence, pp.  1–12, 2023.
  10. Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1):C1–C68, 2018a.
  11. Distribution regression with sample selection, with an application to wage decompositions in the UK. Technical Report 1811.11603, arXiv.org, 2018b.
  12. The sorted effects method: Discovering heterogeneous effects beyond their averages. Econometrica, 86(6):1911–1938, 2018c.
  13. Automatic debiased machine learning via Riesz regression. arXiv preprint arXiv:2104.14737, 2021.
  14. Long Story Short: Omitted Variable Bias in Causal Machine Learning. Technical report, National Bureau of Economic Research, Cambridge, MA, Jul 2022.
  15. Automatic debiased machine learning for dynamic treatment effects and general nested functionals, 2023.
  16. Causally motivated attribution for online advertising. In Proceedings of the sixth international workshop on data mining for online advertising and internet economy, pp.  1–9, 2012.
  17. Causal mediation analysis with multiple mediators. Biometrics, 71(1):1–14, 2015.
  18. Fitting science into legal contexts: Assessing effects of causes or causes of effects? Sociological Methods & Research, 43(3):359–390, 2014.
  19. Efron, B. Prediction, estimation, and attribution. International Statistical Review, 88:S28–S59, 2020.
  20. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press, 2015.
  21. Quantifying intrinsic causal contributions via structure preserving interventions. In International Conference on Artificial Intelligence and Statistics. PMLR, 2024.
  22. A probabilistic multi-touch attribution model for online advertising. In Proceedings of the 25th acm international on conference on information and knowledge management, pp.  1373–1382, 2016.
  23. On measuring causal contributions via d⁢o𝑑𝑜doitalic_d italic_o-interventions. In International Conference on Machine Learning, pp. 10476–10501. PMLR, 2022.
  24. Approximating the shapley value without marginal contributions. Technical Report 2302.00736, arXiv.org, 2023.
  25. Towards explaining distribution shifts. In International Conference on Machine Learning, pp. 17931–17952. PMLR, 2023.
  26. On the need for a language describing distribution shifts: Illustrations on tabular datasets. arXiv preprint arXiv:2307.05284, 2023.
  27. Evaluating causes of effects by posterior effects of causes. Biometrika, 110(2):449–465, 2023.
  28. Explanation shift: Investigating interactions between models and shifting data distributions. arXiv preprint arXiv:2303.08081, 2023.
  29. Cross-fitting and fast remainder rates for semiparametric estimation. arXiv preprint arXiv:1801.09138, 2018.
  30. Predicting good probabilities with supervised learning. In Proceedings of the 22nd international conference on Machine learning, pp.  625–632, 2005.
  31. Oaxaca, R. Male-female wage differentials in urban labor markets. International economic review, pp.  693–709, 1973.
  32. Pearl, J. Causality. Cambridge University Press, 2009.
  33. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017.
  34. Data-driven multi-touch attribution models. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.  258–264, 2011.
  35. Shapley, L. A value for n-person games. Contributions to the theory of games, 1953.
  36. The counterfactual-shapley value: Attributing change in system metrics. In NeurIPS 2022 Workshop on Causality for Real-world Impact, 2022.
  37. Shimodaira, H. Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of statistical planning and inference, 90(2):227–244, 2000.
  38. Causation, Prediction, and Search. MIT press, 2000.
  39. Density Ratio Estimation in Machine Learning. Cambridge University Press, 2012.
  40. Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness, and sensitivity analysis. Annals of Statistics, 40(3):1816, 2012.
  41. Yamamoto, T. Understanding the past: Statistical analysis of causal attribution. American Journal of Political Science, 56(1):237–256, 2012.
  42. Kernel-based conditional independence test and application in causal discovery. In Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, pp.  804–813. AUAI, 2011.
  43. Conditional counterfactual causal effect for individual attribution. In Uncertainty in Artificial Intelligence, pp.  2519–2528. PMLR, 2023.
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