Causal Diagrams for Treatment Effect Estimation with Application to Efficient Covariate Selection

2011 ◽  
Vol 93 (4) ◽  
pp. 1453-1459 ◽  
Author(s):  
Halbert White ◽  
Xun Lu
Keyword(s):  
Treatment Effect ◽  
Covariate Selection ◽  
Causal Diagrams ◽  
Treatment Effect Estimation ◽  
Effect Estimation

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.

scholarly journals On the Estimation of Treatment Effect with Text Covariates

2019 ◽  
Author(s):  
Liuyi Yao ◽  
Sheng Li ◽  
Yaliang Li ◽  
Hongfei Xue ◽  
Jing Gao ◽  
...  
Keyword(s):  
Instrumental Variables ◽  
Treatment Effect ◽  
Potential Outcomes ◽  
Estimation Bias ◽  
Matching Method ◽  
Adversarial Learning ◽  
Treatment Effect Estimation ◽  
Real World Datasets ◽  
Effect Estimation ◽  
Open Question

Estimating the treatment effect benefits decision making in various domains as it can provide the potential outcomes of different choices. Existing work mainly focuses on covariates with numerical values, while how to handle covariates with textual information for treatment effect estimation is still an open question. One major challenge is how to filter out the nearly instrumental variables which are the variables more predictive to the treatment than the outcome. Conditioning on those variables to estimate the treatment effect would amplify the estimation bias. To address this challenge, we propose a conditional treatment-adversarial learning based matching method (CTAM). CTAM incorporates the treatment-adversarial learning to filter out the information related to nearly instrumental variables when learning the representations, and then it performs matching among the learned representations to estimate the treatment effects. The conditional treatment-adversarial learning helps reduce the bias of treatment effect estimation, which is demonstrated by our experimental results on both semi-synthetic and real-world datasets.

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