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Found 9 papers, 2 papers with code
Policy Learning with Distributional Welfare
In this paper, we explore optimal treatment allocation policies that target distributional welfare.
A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning
In this article, we propose a general instrumented DiD approach for learning the optimal treatment policy.
Proximal Causal Learning of Conditional Average Treatment Effects
Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators currently available.
Pessimistic Model Selection for Offline Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications.
Individualized Decision-Making Under Partial Identification: Three Perspectives, Two Optimality Results, and One Paradox
Secondly, building on this unified framework, we provide a novel minimax solution (i. e., a rule that minimizes the maximum regret for so-called opportunists) for individualized decision-making/policy assignment.
On a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable
Unmeasured confounding is a threat to causal inference and individualized decision making.
Statistics Theory Methodology Statistics Theory
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation.
A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity
There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding.
Selective machine learning of doubly robust functionals
While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed.
