Causal estimands are classified by whether they depend only on marginal potential outcome distributions (intervention layer), joint or nested distributions (counterfactual layer), or individual-level outcomes.
Semiparametric Estimation of Long-Term Treatment Effects
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abstract
Long-term outcomes of experimental evaluations are necessarily observed after long delays. We develop semiparametric methods for combining the short-term outcomes of experiments with observational measurements of short-term and long-term outcomes, in order to estimate long-term treatment effects. We characterize semiparametric efficiency bounds for various instances of this problem. These calculations facilitate the construction of several estimators. We analyze the finite-sample performance of these estimators with a simulation calibrated to data from an evaluation of the long-term effects of a poverty alleviation program.
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A Distributional Perspective on Pearl's Causal Hierarchy: From Marginal to Joint and Individualized Potential Outcomes
Causal estimands are classified by whether they depend only on marginal potential outcome distributions (intervention layer), joint or nested distributions (counterfactual layer), or individual-level outcomes.