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stat.ML2022
The Causal Marginal Polytope for Bounding Treatment Effects
Jakob Zeitler, Ricardo Silva
Due to unmeasured confounding, it is often not possible to identify causal effects from a postulated model. Nevertheless, we can ask for partial identification, which usually boils…
stat.ML2022
Stochastic Causal Programming for Bounding Treatment Effects
Kirtan Padh, Jakob Zeitler, David Watson +3
Causal effect estimation is important for many tasks in the natural and social sciences. We design algorithms for the continuous partial identification problem: bounding the effect…