# Proximal Causal Inference for Complex Longitudinal Studies

@inproceedings{Ying2021ProximalCI, title={Proximal Causal Inference for Complex Longitudinal Studies}, author={Andrew Ying and Wang Miao and Xu Shi and Eric J. Tchetgen Tchetgen}, year={2021} }

A standard assumption for causal inference about the joint effects of timevarying treatment is that one has measured sufficient covariates to ensure that within covariate strata, subjects are exchangeable across observed treatment values, also known as “sequential randomization assumption (SRA)”. SRA is often criticized as it requires one to accurately measure all confounders. Realistically, measured covariates can rarely capture all confounders with certainty. Often covariate measurements are…

## 16 Citations

### Causal Inference for Complex Continuous-Time Longitudinal Studies

- Mathematics, Computer Science
- 2022

This work develops a framework to identify causal effects under a user-speciﬁed treatment regime for continuous-time longitudinal studies using generalized consistency assumption, sequential randomization assumption, positivity assumption, and a novel “achievable” assumption designed for continuous time.

### Partial Identification with Noisy Covariates: A Robust Optimization Approach

- Computer ScienceCLeaR
- 2022

This work can formulate the identification of the average treatment effects (ATE) as a robust optimization problem and lead to an efficient robust optimization algorithm that bounds the ATE with noisy covariates, and shows that this robust optimization approach can extend a wide range of causal adjustment methods to perform partial identification.

### Proximal Identification and Estimation to Handle Dependent Right Censoring for Survival Analysis

- Mathematics
- 2022

Modern epidemiological and clinical studies aim at analyzing a time-to-event endpoint. A common complication is right censoring. In some cases, it arises because subjects are still surviving after…

### A Finite Sample Theorem for Longitudinal Causal Inference with Machine Learning: Long Term, Dynamic, and Mediated Effects

- MathematicsArXiv
- 2021

A new multiple robustness to ill posedness for proximal causal inference in longitudinal settings is created and a nonasymptotic theorem for any longitudinal causal parameter estimated with any machine learning algorithm is provided.

### Optimal Individualized Decision-Making with Proxies

- Computer Science, Economics
- 2022

This work proposes a novel optimal individualized treatment regime based on so-called outcome-inducing and treatment-inducing confounding bridges and shows that the value function of this new optimal treatment regime is superior to that of existing ones in the literature.

### Long-term Causal Inference Under Persistent Confounding via Data Combination

- Mathematics, Economics
- 2022

We study the identiﬁcation and estimation of long-term treatment eﬀects when both experimental and observational data are available. Since the long-term outcome is observed only after a long delay,…

### Causal Identification with Matrix Equations

- Computer Science, MathematicsNeurIPS
- 2021

This paper characterize the relationships between certain graphically-driven formulae and matrix multiplications and devise a causal effect identification algorithm, which accepts as input a collection of marginal, conditional, and interventional distributions, integrating enriched matrix-based criteria into a graphical identification approach.

### Doubly Robust Proximal Synthetic Controls

- Economics
- 2022

To infer the treatment eﬀect for a single treated unit using panel data, synthetic control methods search for a linear combination of control units’ outcomes that mimics the treated unit’s…

### Causal Identiﬁcation with Matrix Equations

- Computer Science, Mathematics
- 2022

A new causal effect identiﬁcation algorithm which utilizes both graphical criteria and matrix equations is developed, and a novel intermediary criteria based on the pseudoinverse of a matrix is proposed.

### A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes

- MathematicsICML
- 2022

We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes (POMDPs), where the evaluation policy depends only on observable variables and the behavior policy depends on…

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