Externally Valid Policy Choice

• URL: http://arxiv.org/abs/2205.05561v3
• Date: Sun, 2 Jul 2023 16:16:00 GMT
• Title: Externally Valid Policy Choice
• Authors: Christopher Adjaho and Timothy Christensen
• Abstract summary: We consider the problem of learning personalized treatment policies that are externally valid or generalizable. We first show that welfare-maximizing policies for the experimental population are robust to shifts in the distribution of outcomes. We then develop new methods for learning policies that are robust to shifts in outcomes and characteristics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of learning personalized treatment policies that are externally valid or generalizable: they perform well in other target populations besides the experimental (or training) population from which data are sampled. We first show that welfare-maximizing policies for the experimental population are robust to shifts in the distribution of outcomes (but not characteristics) between the experimental and target populations. We then develop new methods for learning policies that are robust to shifts in outcomes and characteristics. In doing so, we highlight how treatment effect heterogeneity within the experimental population affects the generalizability of policies. Our methods may be used with experimental or observational data (where treatment is endogenous). Many of our methods can be implemented with linear programming.

Related papers

• Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
  In practice, causal researchers do not have a single outcome in mind a priori. In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
  arXiv  Detail & Related papers  (2024-04-29T08:16:30Z)
• Adaptive Instrument Design for Indirect Experiments [48.815194906471405]
  Unlike RCTs, indirect experiments estimate treatment effects by leveragingconditional instrumental variables. In this paper we take the initial steps towards enhancing sample efficiency for indirect experiments by adaptively designing a data collection policy. Our main contribution is a practical computational procedure that utilizes influence functions to search for an optimal data collection policy.
  arXiv  Detail & Related papers  (2023-12-05T02:38:04Z)
• Policy Learning with Distributional Welfare [1.0742675209112622]
  Most literature on treatment choice has considered utilitarian welfare based on the conditional average treatment effect (ATE) This paper proposes an optimal policy that allocates the treatment based on the conditional quantile of individual treatment effects (QoTE)
  arXiv  Detail & Related papers  (2023-11-27T14:51:30Z)
• Externally Valid Policy Evaluation Combining Trial and Observational Data [6.875312133832077]
  We seek to use trial data to draw valid inferences about the outcome of a policy on the target population. We develop a method that yields certifiably valid trial-based policy evaluations under any specified range of model miscalibrations.
  arXiv  Detail & Related papers  (2023-10-23T10:01:50Z)
• Effect-Invariant Mechanisms for Policy Generalization [3.701112941066256]
  It has been suggested to exploit invariant conditional distributions to learn models that generalize better to unseen environments. We introduce a relaxation of full invariance called effect-invariance and prove that it is sufficient, under suitable assumptions, for zero-shot policy generalization. We present empirical results using simulated data and a mobile health intervention dataset to demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-19T14:50:24Z)
• Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection [56.87650511573298]
  We propose a general framework called Learnable Behavioral Control (LBC) to address the limitation. Our agents have achieved 10077.52% mean human normalized score and surpassed 24 human world records within 1B training frames.
  arXiv  Detail & Related papers  (2023-05-09T08:00:23Z)
• Conformal Off-Policy Evaluation in Markov Decision Processes [53.786439742572995]
  Reinforcement Learning aims at identifying and evaluating efficient control policies from data. Most methods for this learning task, referred to as Off-Policy Evaluation (OPE), do not come with accuracy and certainty guarantees. We present a novel OPE method based on Conformal Prediction that outputs an interval containing the true reward of the target policy with a prescribed level of certainty.
  arXiv  Detail & Related papers  (2023-04-05T16:45:11Z)
• Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality [94.89246810243053]
  This paper studies offline policy learning, which aims at utilizing observations collected a priori to learn an optimal individualized decision rule. Existing policy learning methods rely on a uniform overlap assumption, i.e., the propensities of exploring all actions for all individual characteristics must be lower bounded. We propose Pessimistic Policy Learning (PPL), a new algorithm that optimize lower confidence bounds (LCBs) instead of point estimates.
  arXiv  Detail & Related papers  (2022-12-19T22:43:08Z)
• Generalizing Off-Policy Learning under Sample Selection Bias [15.733136147164032]
  We propose a novel framework for learning policies that generalize to the target population. We prove that, if the uncertainty set is well-specified, our policies generalize to the target population as they can not do worse than on the training data.
  arXiv  Detail & Related papers  (2021-12-02T16:18:16Z)
• Policy design in experiments with unknown interference [0.0]
  We study estimation and inference on policies with spillover effects. Units are organized into a finite number of large clusters. We provide strong theoretical guarantees and an implementation in a large-scale field experiment.
  arXiv  Detail & Related papers  (2020-11-16T18:58:54Z)
• Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
  Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
  arXiv  Detail & Related papers  (2020-06-14T01:15:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.