Estimation of Causal Effects of Multiple Treatments in Observational Studies with a Binary Outcome

by   Liangyuan Hu, et al.

There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian Additive Regression Trees (BART) in such settings. First, we compare BART to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting (IPTW), targeted maximum likelihood estimator (TMLE), vector matching and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the treatment assignment and outcome generating mechanisms, BART, TMLE and IPTW using generalized boosted models (GBM) provide better bias reduction and smaller root mean squared error. BART and TMLE provide more consistent 95 per cent CI coverage and better large-sample convergence property. Second, we supply BART with a strategy to identify a common support region for retaining inferential units and for avoiding extrapolating over areas of the covariate space where common support does not exist. BART retains more inferential units than the generalized propensity score based strategy, and shows lower bias, compared to TMLE or GBM, in a variety of scenarios differing by the degree of covariate overlap. A case study examining the effects of three surgical approaches for non-small cell lung cancer demonstrates the methods.


page 1

page 2

page 3

page 4


CIMTx: An R package for causal inference with multiple treatments using observational data

CIMTx provides efficient and unified functions to implement modern metho...

Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes

The preponderance of large-scale healthcare databases provide abundant o...

The Estimation of Causal Effects of Multiple Treatments in Observational Studies Using Bayesian Additive Regression Trees

There is currently a dearth of appropriate methods to estimate the causa...

Matching on Generalized Propensity Scores with Continuous Exposures

Generalized propensity scores (GPS) are commonly used when estimating th...

Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation

There are now many options for doubly robust estimation; however, there ...

An ensemble approach to improved prediction from multitype data

We have developed a strategy for the analysis of newly available binary ...

Please sign up or login with your details

Forgot password? Click here to reset