scholarly journals Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data

Biometrics ◽  
2021 ◽  
Author(s):  
Jiabei Yang ◽  
Issa J. Dahabreh ◽  
Jon A. Steingrimsson
Keyword(s):  
Observational Data ◽  
Treatment Effects ◽  
Causal Interaction ◽  
Heterogeneous Treatment Effects

Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases

2018 ◽  
Vol 37 (23) ◽  
pp. 3309-3324 ◽  
Author(s):  
T. Wendling ◽  
K. Jung ◽  
A. Callahan ◽  
A. Schuler ◽  
N. H. Shah ◽  
...  
Keyword(s):  
Health Care ◽  
Observational Data ◽  
Treatment Effects ◽  
Heterogeneous Treatment Effects

An Instrumental Variable Forest Approach for Detecting Heterogeneous Treatment Effects in Observational Studies

2021 ◽  
Author(s):  
Guihua Wang ◽  
Jun Li ◽  
Wallace J. Hopp
Keyword(s):  
Observational Data ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Mean Squared Error ◽  
Big Data Analytics ◽  
Simulated Data ◽  
Teaching Hospitals ◽  
Heterogeneous Treatment Effects ◽  
Similar Treatment ◽  
Targeted Marketing

This study addresses the ubiquitous challenge of using big observational data to identify heterogeneous treatment effects. This problem arises in precision medicine, targeted marketing, personalized education, and many other environments. Identifying heterogeneous treatment effects presents several analytical challenges including high dimensionality and endogeneity issues. We develop a new instrumental variable tree (IVT) approach that incorporates the instrumental variable method into a causal tree (CT) to correct for potential endogeneity biases that may exist in observational data. Our IVT approach partitions subjects into subgroups with similar treatment effects within subgroups and different treatment effects across subgroups. The estimated treatment effects are asymptotically consistent under a set of mild assumptions. Using simulated data, we show our approach has a better coverage rate and smaller mean-squared error than the conventional CT approach. We also demonstrate that an instrumental variable forest (IVF) constructed using IVTs has better accuracy and stratification than a generalized random forest. Finally, by applying the IVF approach to an empirical assessment of laparoscopic colectomy, we demonstrate the importance of accounting for endogeneity to make accurate comparisons of the heterogeneous effects of the treatment (teaching hospitals) and control (nonteaching hospitals) on different types of patients. This paper was accepted by J. George Shanthikumar, big data analytics.

Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods

2017 ◽  
Vol 25 (4) ◽  
pp. 413-434 ◽  
Author(s):  
Justin Grimmer ◽  
Solomon Messing ◽  
Sean J. Westwood
Keyword(s):  
Treatment Effects ◽  
Ensemble Methods ◽  
Ensemble Method ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Randomized Experiments ◽  
Heterogeneous Treatment Effects ◽  
Data Set ◽  
Heterogeneous Effects ◽  
The Ensemble Method

Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across subpopulations—heterogeneous treatment effects—and how differences in the content of the treatment affects responses—the response to heterogeneous treatments. Several new methods have been introduced to estimate heterogeneous effects, but it is difficult to know if a method will perform well for a particular data set. Rather than using only one method, we show how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects. Building on a large literature on ensemble methods, we show how the weighting of methods can contribute to accurate estimation of heterogeneous treatment effects and demonstrate how pooling models lead to superior performance to individual methods across diverse problems. We apply the ensemble method to two experiments, illuminating how the ensemble method for heterogeneous treatment effects facilitates exploratory analysis of treatment effects.

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