Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation

By Günter J. Hitsch, Sanjog Misra. 2018

96 citations recorded by the source corpus.

Subfields: Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Cynthia Rudin
2. Alexander Volfovsky
3. Sudeepa Roy
4. Tengyuan Liang
5. Sanjog Misra
6. Max H. Farrell
7. M. D. Cattaneo
8. Zhiqiang Tan

Citations in the corpus

• FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, methodology): “, 2019) suggests that CATEs estimated by manually calibrated methods can be better than CATEs estimated by black box models, even though the latter can achieve good performance if they are implemented with careful consideration of the specific application (Hitsch and Misra, 2018).”
• Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, methodology): “Hitsch and Misra (2018) recently used this data to study various estimators, both traditional and modern, of heterogeneous treatment effects.”
• DEEP NEURAL NETWORKS FOR ESTIMATION AND INFERENCE (2020, methodology): “Hitsch and Misra (2018) recently used this data to study various estimators, both traditional and modern, of heterogeneous treatment effects.”