Apr 14, 2021
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Assumptions about equality of effects, across time periods or across individuals are commonly made in causal inference tasks. For example, the difference-in-differences method assumes that confounding remains the same across time periods. Similarly, it is often reasonable to assume that causal effects apply equally to units undergoing interference. Finally, sensitivity analysis often hypothesizes equality among existing and unaccounted for confounders. Despite the ubiquity of these ``equality constraints'', modern identification methods have not leveraged their presence in a systematic way. In this paper, we provide a technique that enables graphical identification methods to utilize these additional constraints, and demonstrate its superiority over existing methods. Specifically, we demonstrate improvements in identification power over difference-in-differences methods, and under interference conditions. We further generalize existing methods for benchmarking in sensitivity analysis.Assumptions about equality of effects, across time periods or across individuals are commonly made in causal inference tasks. For example, the difference-in-differences method assumes that confounding remains the same across time periods. Similarly, it is often reasonable to assume that causal effects apply equally to units undergoing interference. Finally, sensitivity analysis often hypothesizes equality among existing and unaccounted for confounders. Despite the ubiquity of these ``equality co…
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The 24th International Conference on Artificial Intelligence and Statistics was held virtually from Tuesday, 13 April 2021 to Thursday, 15 April 2021.
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