Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding

@article{Qi2021ProximalLF,
  title={Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding},
  author={Zhengling Qi and Rui Miao and Xiaoke Zhang},
  journal={ArXiv},
  year={2021},
  volume={abs/2105.01187}
}
Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in observational studies. Motivated by the recent proposed proximal causal inference, we develop several proximal learning approaches to estimating optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. In particular, we establish… 

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References

SHOWING 1-10 OF 63 REFERENCES

A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity

A general instrumental variable approach to learning optimal treatment regimes under endogeneity is proposed and establishes identification of both value function for a given regime and optimal regimes with the aid of a binary IV, when no unmeasured confounding fails to hold.

Semiparametric proximal causal inference

This paper considers the framework of proximal causal inference introduced by Tchetgen Tchet Gen et al. (2020), which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on the basis of measured covariates fails.

Confounding-Robust Policy Improvement

It is demonstrated that hidden confounding can hinder existing policy learning approaches and lead to unwarranted harm, while the robust approach guarantees safety and focuses on well-evidenced improvement, a necessity for making personalized treatment policies learned from observational data reliable in practice.

Efficient augmentation and relaxation learning for individualized treatment rules using observational data

This work considers a class of estimators for optimal treatment rules that are analogous to convex large-margin classifiers and derives rates of convergence for the proposed estimators and uses these rates to characterize the bias-variance trade-off for estimating individualized treatment rules with classification-based methods.

Policy Learning With Observational Data

Given a doubly robust estimator of the causal effect of assigning everyone to treatment, an algorithm for choosing whom to treat is developed, and strong guarantees for the asymptotic utilitarian regret of the resulting policy are established.

An Introduction to Proximal Causal Learning

A formal potential outcome framework for proximal causal learning is introduced, which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on the basis of measured covariates fails.

Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach

This paper tackles the primary challenge to causal inference using negative controls: the identification and estimation of these bridge functions, and provides a new identification strategy that avoids both uniqueness and completeness.

Bias Formulas for Violations of Proximal Identification Assumptions

Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to

Multiply robust causal inference with double‐negative control adjustment for categorical unmeasured confounding

This work establishes non-parametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables, and provides a general semiparametric framework for obtaining inferences about the AtE while leveraging information about a possibly large number of measured covariates.

Quantile-Optimal Treatment Regimes

This article proposes an alternative formulation of the estimator as a solution of an optimization problem with an estimated nuisance parameter, and derives theory involving a nonstandard convergence rate and a nonnormal limiting distribution of the quantile-optimal treatment regime.
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