scholarly journals Inference of heterogeneous treatment effects using observational data with high‐dimensional covariates

2021 ◽  
Author(s):  
Yumou Qiu ◽  
Jing Tao ◽  
Xiao‐Hua Zhou
Keyword(s):  
Observational Data ◽  
Treatment Effects ◽  
High Dimensional ◽  
Heterogeneous Treatment Effects

scholarly journals NM2 Using Randomized and Observational DATA to Predict Heterogeneous Treatment Effects

2020 ◽  
Vol 23 ◽  
pp. S406-S407
Author(s):  
K. Chalkou ◽  
F. Pellegrini ◽  
G. Salanti
Keyword(s):  
Observational Data ◽  
Treatment Effects ◽  
Heterogeneous Treatment Effects

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

scholarly journals Estimating Heterogeneous Treatment Effects with Observational Data

2012 ◽  
Vol 42 (1) ◽  
pp. 314-347 ◽  
Author(s):  
Yu Xie ◽  
Jennie E. Brand ◽  
Ben Jann
Keyword(s):  
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.

Modeling heterogeneous treatment effects in the presence of endogeneity

2021 ◽  
pp. 1-14
Author(s):  
Giacomo Benini ◽  
Stefan Sperlich
Keyword(s):  
Treatment Effects ◽  
Heterogeneous Treatment Effects

Inference for treatment effect parameters in potentially misspecified high-dimensional models

Biometrika ◽  
2020 ◽  
Author(s):  
Oliver Dukes ◽  
Stijn Vansteelandt
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Observational Studies ◽  
Treatment Effects ◽  
Treatment Selection ◽  
High Dimensional ◽  
Selection Mechanism ◽  
Dimensional Models ◽  
Effect Parameter

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.

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