DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
On the role of the propensity score in efficient semiparametric estimation of average treatment effects.Econometrica, 66(2):315–331
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Advocates applying causal inference to preference learning for LLM alignment to diagnose generalization failures and guide better data practices.
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Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
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Advocates applying causal inference to preference learning for LLM alignment to diagnose generalization failures and guide better data practices.