• Corpus ID: 237513551

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… 

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References

SHOWING 1-10 OF 65 REFERENCES

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.

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.

Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments

Even in the absence of unmeasured confounding factors or model misspecification, standard methods for estimating the causal effect of time-varying treatments on survival are biased when (a) there

CONFOUNDER ADJUSTMENT IN MULTIPLE HYPOTHESIS TESTING.

This work provides theoretical guarantees for RUV-4 and LEAPP and shows that if the confounding factors are strong, the resulting estimators can be asymptotically as powerful as the oracle estimator which observes the latent confounding factors.

A Confounding Bridge Approach for Double Negative Control Inference on Causal Effects (Supplement and Sample Codes are included)

Unmeasured confounding is a key challenge for causal inference. Negative control variables are widely available in observational studies. A negative control outcome is associated with the confounder

The Control Outcome Calibration Approach for Causal Inference With Unobserved Confounding

This paper proposes to use control outcomes in a simple but formal counterfactual-based approach to correct causal effect estimates for bias due to unobserved confounding and develops a sensitivity analysis technique.

Proxy Controls and Panel Data.

A flexible approach to the identification and estimation of causal objects in nonparametric, non-separable models with confounding with the use of `proxy controls': covariates that do not satisfy a standard `unconfoundedness' assumption but which are informative proxies for variables that do.

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.

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.

A New Method for Partial Correction of Residual Confounding in Time-Series and Other Observational Studies

Methods exist to detect residual confounding in epidemiologic studies. One requires a negative control exposure with 2 key properties: 1) conditional independence of the negative control and the
...