Paper Info
Title
Ace: Adaptively Similarity-Preserved Representation Learning For Individual Treatment Effect Estimation
Abstract
Treatment effect estimation refers to the estimation of causal effects, which benefits decision-making process across various domains, but it is a challenging problem in real practice. The estimation of causal effects from observational data at the individual level faces two major challenges, i.e., treatment selection bias and missing counterfactuals. Existing methods tackle the selection bias problem by learning a balanced representation and infer the missing counterfactuals based on the learned representation. However, most existing methods learn the representation in a global manner and ignore the local similarity information, which is essential for an accurate estimation of causal effects. Motivated by the above observations, we propose a novel representation learning method, which adaptively extracts fine-grained similarity information from the original feature space and minimizes the distance between different treatment groups as well as the similarity loss during the representation learning procedure. Experiments on three public datasets demonstrate that the proposed method achieves the best performance in causal effect estimation among all the compared methods and is robust to the treatment selection bias.
Year
DOI
Venue
2019
10.1109/ICDM.2019.00186
2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019)
Keywords
Field
DocType
treatment effect estimation, similarity preserving, representation learning
Treatment and control groups,Data mining,Observational study,Feature vector,Computer science,Counterfactual conditional,Causal effect,Artificial intelligence,Treatment effect,Feature learning,Selection bias,Machine learning
Conference
ISSN
Citations 
PageRank 
1550-4786
1
0.36
References 
Authors
0
6
Name
Order
Citations
PageRank
Liuyi Yao1125.00
Sheng Li260953.39
yaliang li362950.87
Mengdi Huai42910.02
Jing Gao52723131.05
Aidong Zhang62970405.63