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Multiply-Robust Causal Change Attribution
April 16, 2024, 4:43 a.m. | Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, Dominik Janzing, David Heckerman
cs.LG updates on arXiv.org arxiv.org
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 …
abstract arxiv attribution causal change cs.lg data distribution econ.em explained multiple observe regression robust samples stat.me stat.ml strategy type variables
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