A Short Survey on Forest Based Heterogeneous Treatment Effect Estimation Methods: Meta-learners and Specific Models

2021 ◽  
Author(s):  
Hao Jiang ◽  
Peng Qi ◽  
Jingying Zhou ◽  
Jack Zhou ◽  
Sharath Rao
Keyword(s):  
Treatment Effect ◽  
Estimation Methods ◽  
Short Survey ◽  
Treatment Effect Estimation ◽  
Effect Estimation ◽  
Heterogeneous Treatment Effect

scholarly journals Some methods for heterogeneous treatment effect estimation in high dimensions

2018 ◽  
Vol 37 (11) ◽  
pp. 1767-1787 ◽  
Author(s):  
Scott Powers ◽  
Junyang Qian ◽  
Kenneth Jung ◽  
Alejandro Schuler ◽  
Nigam H. Shah ◽  
...  
Keyword(s):  
Treatment Effect ◽  
High Dimensions ◽  
Treatment Effect Estimation ◽  
Effect Estimation ◽  
Heterogeneous Treatment Effect

scholarly journals Comparing the Performance of Statistical Adjustment Methods by Recovering the Experimental Benchmark from the REFLUX Trial

2021 ◽  
pp. 0272989X2098654
Author(s):  
Luke Keele ◽  
Stephen O’Neill ◽  
Richard Grieve
Keyword(s):  
Patient Preference ◽  
Treatment Effect ◽  
Medical Management ◽  
Observational Studies ◽  
Treatment Effectiveness ◽  
Average Treatment Effect ◽  
Estimation Methods ◽  
Effect Estimate ◽  
Treatment Effect Estimation ◽  
Effect Estimation

Much evidence in comparative effectiveness research is based on observational studies. Researchers who conduct observational studies typically assume that there are no unobservable differences between the treatment groups under comparison. Treatment effectiveness is estimated after adjusting for observed differences between comparison groups. However, estimates of treatment effectiveness may be biased because of misspecification of the statistical model. That is, if the method of treatment effect estimation imposes unduly strong functional form assumptions, treatment effect estimates may be inaccurate, leading to inappropriate recommendations about treatment decisions. We compare the performance of a wide variety of treatment effect estimation methods for the average treatment effect. We do so within the context of the REFLUX study from the United Kingdom. In REFLUX, participants were enrolled in either an randomized controlled trial (RCT) or an observational study arm. In the RCT, patients were randomly assigned to either surgery or medical management. In the patient preference arm, participants selected to either have surgery or medical management. We attempt to recover the treatment effect estimate from the RCT using the data from the patient preference arms of the study. We vary the method of treatment effect estimation and record which methods are successful and which are not. We apply more than 20 different methods, including standard regression models as well as advanced machine learning methods. We find that simple propensity score matching methods provide the least accurate estimates versus the RCT benchmark. We find variation in performance across the other methods, with some, but not all recovering the experimental benchmark. We conclude that future studies should use multiple methods of estimation to fully represent uncertainty according to the choice of estimation approach.

On learning disentangled representations for individual treatment effect estimation

2021 ◽  
pp. 103940
Author(s):  
Jiebin Chu ◽  
Zhoujian Sun ◽  
Wei Dong ◽  
Jinlong Shi ◽  
Zhengxing Huang
Keyword(s):  
Treatment Effect ◽  
Individual Treatment ◽  
Treatment Effect Estimation ◽  
Effect Estimation

scholarly journals A Distillation Approach to Data Efficient Individual Treatment Effect Estimation

2019 ◽  
Vol 33 ◽  
pp. 4544-4551 ◽  
Author(s):  
Maggie Makar ◽  
Adith Swaminathan ◽  
Emre Kıcıman
Keyword(s):  
Treatment Effect ◽  
Newborn Health ◽  
Real Data ◽  
Machine Learning Algorithms ◽  
Test Time ◽  
Individual Treatment ◽  
Test Case ◽  
Treatment Effect Estimation ◽  
Synthetic Datasets ◽  
Effect Estimation

The potential for using machine learning algorithms as a tool for suggesting optimal interventions has fueled significant interest in developing methods for estimating heterogeneous or individual treatment effects (ITEs) from observational data. While several methods for estimating ITEs have been recently suggested, these methods assume no constraints on the availability of data at the time of deployment or test time. This assumption is unrealistic in settings where data acquisition is a significant part of the analysis pipeline, meaning data about a test case has to be collected in order to predict the ITE. In this work, we present Data Efficient Individual Treatment Effect Estimation (DEITEE), a method which exploits the idea that adjusting for confounding, and hence collecting information about confounders, is not necessary at test time. DEITEE allows the development of rich models that exploit all variables at train time but identifies a minimal set of variables required to estimate the ITE at test time. Using 77 semi-synthetic datasets with varying data generating processes, we show that DEITEE achieves significant reductions in the number of variables required at test time with little to no loss in accuracy. Using real data, we demonstrate the utility of our approach in helping soon-to-be mothers make planning and lifestyle decisions that will impact newborn health.

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