Semiparametric Double Reinforcement Learning with Applications to Long-Term Causal Inference

TL;DR

This paper introduces a semiparametric extension of Double Reinforcement Learning that relaxes overlap conditions and improves efficiency in long-term causal inference, avoiding complex density-ratio estimation.

Contribution

It develops doubly robust, automatic estimators for linear functionals of the Q-function in infinite-horizon MDPs, reducing high-dimensional challenges to a single dimension.

Findings

Achieves efficiency gains over nonparametric methods.

Reduces overlap condition to a single-dimensional requirement.

Avoids density-ratio estimation by using Q-function as a summary.

Abstract

Double Reinforcement Learning (DRL) enables efficient inference for policy values in nonparametric Markov decision processes (MDPs), but existing methods face two major obstacles: (1) they require stringent intertemporal overlap conditions on state trajectories, and (2) they rely on estimating high-dimensional occupancy density ratios. Motivated by problems in long-term causal inference, we extend DRL to a semiparametric setting and develop doubly robust, automatic estimators for general linear functionals of the Q-function in infinite-horizon, time-homogeneous MDPs. By imposing structure on the Q-function, we relax the overlap conditions required by nonparametric methods and obtain efficiency gains. The second obstacle--density-ratio estimation--typically requires computationally expensive and unstable min-max optimization. To address both challenges, we introduce superefficient nonparametric estimators whose limiting variance falls below the generalized Cramer-Rao bound. These estimators treat the Q-function as a one-dimensional summary of the state-action process, reducing high-dimensional overlap requirements to a single-dimensional condition. The procedure is simple to implement: estimate and calibrate the Q-function using fitted Q-iteration, then plug the result into the target functional, thereby avoiding density-ratio estimation altogether.