Multiply-Robust Causal Change Attribution
This work addresses the challenge of causal change attribution for researchers and practitioners in fields like economics or social sciences, offering a robust method that can be integrated into existing frameworks, though it appears incremental as it builds on prior causal attribution techniques.
The paper tackles the problem of attributing changes in an outcome distribution to multiple explanatory variables by developing a multiply robust estimation strategy that combines regression and re-weighting methods, proving consistency and asymptotic normality, and demonstrating excellent performance in simulations and an empirical application.
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).