NDLI: Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing

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Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing

Content Provider	ACM Digital Library
Author	Kuang, Kun Cui, Peng Jiang, Meng Li, Bo Yang, Shiqiang
Abstract	Estimating treatment effect plays an important role on decision making in many fields, such as social marketing, healthcare, and public policy. The key challenge on estimating treatment effect in the wild observational studies is to handle confounding bias induced by imbalance of the confounder distributions between treated and control units. Traditional methods remove confounding bias by re-weighting units with supposedly accurate propensity score estimation under the unconfoundedness assumption. Controlling high-dimensional variables may make the unconfoundedness assumption more plausible, but poses new challenge on accurate propensity score estimation. One strand of recent literature seeks to directly optimize weights to balance confounder distributions, bypassing propensity score estimation. But existing balancing methods fail to do selection and differentiation among the pool of a large number of potential confounders, leading to possible underperformance in many high dimensional settings. In this paper, we propose a data-driven Differentiated Confounder Balancing (DCB) algorithm to jointly select confounders, differentiate weights of confounders and balance confounder distributions for treatment effect estimation in the wild high dimensional settings. The synergistic learning algorithm we proposed is more capable of reducing the confounding bias in many observational studies. To validate the effectiveness of our DCB algorithm, we conduct extensive experiments on both synthetic and real datasets. The experimental results clearly demonstrate that our DCB algorithm outperforms the state-of-the-art methods. We further show that the top features ranked by our algorithm generate accurate prediction of online advertising effect.
Starting Page	265
Ending Page	274
Page Count	10
File Format	PDF
ISBN	9781450348874
DOI	10.1145/3097983.3098032
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Causal inference High dimensional inference Treatment effect estimation
Content Type	Text
Resource Type	Article

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