Bayesian Estimation and Sensitivity Analysis for Causal Inference

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This disseration aims to explore Bayesian methods for causal inference. In chapter 1, we present an overview of fundamental ideas from causal inference along with an outline of the methodological developments that we hope to tackle. In chapter 2, we develop a Gaussian-process mixture model for heterogeneous treatment effect estimation that leverages the use of transformed outcomes. The approach we will present attempts to improve point estimation and uncertainty quantification relative to past work that has used transformed variable related methods as well as traditional outcome modeling. Earlier work on modeling treatment effect heterogeneity using transformed outcomes has relied on tree based methods such as single regression trees and random forests. Under the umbrella of non-parametric models, outcome modeling has been performed using Bayesian additive regression trees and various flavors of weighted single trees. These approaches work well when large samples are available, but suffer in smaller samples where results are more sensitive to model misspecification -- our method attempts to garner improvements in inference quality via a correctly specified model rooted in Bayesian non-parametrics. Furthermore, while we begin with a model that assumes that the treatment assignment mechanism is known, an extension where it is learnt from the data is presented for applications to observational studies. Our approach is applied to simulated and real data to demonstrate our theorized improvements in inference with respect to two causal estimands: the conditional average treatment effect and the average treatment effect. By leveraging our correctly specified model, we are able to more accurately estimate the treatment effects while reducing their variance. In chapter 3, we parametrically and hierarchically estimate the average causal effects of different lengths of stay in the Udayan Ghar Program under the assumption that selection into different lengths is based on a set of observed covariates. This program was piloted in New Delhi, India as a means of providing a residential surrogate to vulnerable and at risk children with the hope of improving their psychological development. We find that the estimated effects on the psychological ideas of self concept and ego resilience (measured by the standardized Piers-Harris score) increase with the length of the time spent in the program. We are also able to conclude that there are measurable differences that exist between male and female children that spend time in the program. In chapter 4, we supplement the estimation of hierarchical dose-response function estimation by introducing a novel sensitivity-analysis and summarization strategy for assessing the robustness of our results to violations of the assumption of unconfoundedness. Finally, in chapter 5, we summarize what this dissertation has achieved, and briefly outline important areas where our work warrants further development.





Zaidi, Abbas M (2019). Bayesian Estimation and Sensitivity Analysis for Causal Inference. Dissertation, Duke University. Retrieved from


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