A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity

Cui, Yifan; Tchetgen, Eric Tchetgen

doi:10.1080/01621459.2020.1783272

Cited by 54 publications

(95 citation statements)

References 62 publications

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“…Some versions of IV identification assumptions, for instance those established in Wang and Tchetgen Tchetgen (2018), would allow a valid IV to point identify the population average treatment effect. Estimating optimal treatment rules when the CATE can be point identified using a valid IV has been carefully studied in Cui and Tchetgen Tchetgen (2020) and Qiu et al. (2020), and is not the focus of the current paper, although it is a special case of our general framework.…”

Section: Itr Estimation With An Iv: From Point To Partial Identificationmentioning

confidence: 99%

“…One key ingredient in ITR estimation problems is to estimate the average treatment effect conditional on patients’ clinical and prognostic features. However, estimation of the conditional average treatment effect (CATE) can be challenging in randomized control trials (RCTs) with high‐dimensional covariates, limited sample size and individual non‐compliance, and observational studies due to the universal concern of unmeasured confounding (Cui & Tchetgen Tchetgen, 2020; Kallus & Zhou, 2018; Kallus et al., 2019; Qiu et al., 2020; Zhang et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

“…However, few works have explored using an IV to estimate optimal ITRs. Two exceptions are Cui and Tchetgen Tchetgen (2020) and Qiu et al. (2020), both of which studied ITR estimation problems when an IV can be used to point identify the conditional average treatment effect under assumptions introduced in Wang and Tchetgen Tchetgen (2018).…”

Section: Introductionmentioning

confidence: 99%

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Estimating Optimal Treatment Rules with an Instrumental Variable: A Partial Identification Learning Approach

Zhang

2021

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Individualized treatment rules (ITRs) are considered a promising recipe to deliver better policy interventions. One key ingredient in optimal ITR estimation problems is to estimate the average treatment effect conditional on a subject’s covariate information, which is often challenging in observational studies due to the universal concern of unmeasured confounding. Instrumental variables (IVs) are widely used tools to infer the treatment effect when there is unmeasured confounding between the treatment and outcome. In this work, we propose a general framework of approaching the optimal ITR estimation problem when a valid IV is allowed to only partially identify the treatment effect. We introduce a novel notion of optimality called ‘IV‐optimality’. A treatment rule is said to be IV‐optimal if it minimizes the maximum risk with respect to the putative IV and the set of IV identification assumptions. We derive a bound on the risk of an IV‐optimal rule that illuminates when an IV‐optimal rule has favourable generalization performance. We propose a classification‐based statistical learning method that estimates such an IV‐optimal rule, design computationally efficient algorithms, and prove theoretical guarantees. We contrast our proposed method to the popular outcome weighted learning (OWL) approach via extensive simulations, and apply our method to study which mothers would benefit from travelling to deliver their premature babies at hospitals with high‐level neonatal intensive care units. R package ivitr implements the proposed method.

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Section: Itr Estimation With An Iv: From Point To Partial Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating Optimal Treatment Rules with an Instrumental Variable: A Partial Identification Learning Approach

Zhang

2021

Journal of the Royal Statistical Society Series B: Statistical Methodology

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“…This paper brings that literature to observational contexts. Recently, Han (forthcoming), Cui and Tchetgen Tchetgen (2020) and Qiu et al (2020) relax sequential randomization and establish identification of dynamic average treatment effects and/or optimal regimes using instrumental variables. They consider a regime that is a mapping only from covariates, but not previous outcomes and treatments, to an allocation.…”

Section: Introductionmentioning

confidence: 99%

“…They consider a regime that is a mapping only from covariates, but not previous outcomes and treatments, to an allocation. They focus on point identification by imposing assumptions such as the existence of additional exogenous variables in a multi-period setup (Han (forthcoming)) or the zero correlation between unmeasured confounders and compliance types in a single-period setup (Cui and Tchetgen Tchetgen (2020); Qiu et al (2020)). Relatedly, the dynamic effects of treatment timing (i.e., irreversible treatments) have been considered in Heckman and Navarro (2007) and Heckman et al (2016) who utilize exclusion restrictions and infinite support assumptions, and in Athey and Imbens (2018), Callaway and Sant'Anna (2019), and Abraham and Sun (2020), who extend the difference-in-differences approach to dynamic settings.…”

Section: Introductionmentioning

confidence: 99%

Optimal dynamic treatment regimes and partial welfare ordering

Han¹

2020

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Dynamic treatment regimes are treatment allocations tailored to heterogeneous individuals. The optimal dynamic treatment regime is a regime that maximizes counterfactual welfare. We introduce a framework in which we can partially learn the optimal dynamic regime from observational data, relaxing the sequential randomization assumption commonly employed in the literature but instead using (binary) instrumental variables. We propose the notion of sharp partial ordering of counterfactual welfares with respect to dynamic regimes and establish mapping from data to partial ordering via a set of linear programs. We then characterize the identified set of the optimal regime as the set of maximal elements associated with the partial ordering. One main contribution of this paper is that we develop simple analytical conditions to establish the ordering, which bypass solving a large number of large-scale linear programs, and thus facilitate estimation and inference. This paper's analytical framework has broader applicability beyond the current context, e.g., in establishing signs of various treatment effects and rankings of policies across different counterfactual scenarios.

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Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes

2023

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Data-driven methods for personalizing treatment assignment have garnered much attention from clinicians and researchers. Dynamic treatment regimes formalize this through a sequence of decision rules that map individual patient characteristics to a recommended treatment. Observational studies are commonly used for estimating dynamic treatment regimes due to the potentially prohibitive costs of conducting sequential multiple assignment randomized trials. However, estimating a dynamic treatment regime from observational data can lead to bias in the estimated regime due to unmeasured confounding. Sensitivity analyses are useful for assessing how robust the conclusions of the study are to a potential unmeasured confounder. A Monte Carlo sensitivity analysis is a probabilistic approach that involves positing and sampling from distributions for the parameters governing the bias. We propose a method for performing a Monte Carlo sensitivity analysis of the bias due to unmeasured confounding in the estimation of dynamic treatment regimes. We demonstrate the performance of the proposed procedure with a simulation study and apply it to an observational study examining tailoring the use of antidepressant medication for reducing symptoms of depression using data from Kaiser Permanente Washington.

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A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity

Cited by 54 publications

References 62 publications

Estimating Optimal Treatment Rules with an Instrumental Variable: A Partial Identification Learning Approach

Estimating Optimal Treatment Rules with an Instrumental Variable: A Partial Identification Learning Approach

Optimal dynamic treatment regimes and partial welfare ordering

Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes

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