Abstract
For causal inference, an important issue is to estimate the treatment effect from observational data where variables are confounded. The matching method is a classical algorithm for controlling the confounding bias via matching units with different treatments but similar variables. But traditional matching methods fail to do selection and differentiation among the pool of a large number of potential confounders, leading to possible underperformance in high dimensional settings. In this paper, we give a new theoretical analysis on confounder selection and differentiation, and propose a novel Differentiated Confounder Matching (DCM) algorithm for both individual and average treatment effect estimation by optimizing confounder weights and units matching. With extensive experiments on both synthetic and real-world datasets, we demonstrate that our DCM algorithm achieves significantly better performance than other matching methods on both individual and average treatment effect estimation.
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Notes
- 1.
The linearity assumption can be relaxed by including high order terms of \(\mathbf {X}\) inregression.
- 2.
High dimension brings NULL matching in DAME method, we compare our algorithm with other baselines in high dimensional continuous setting.
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Acknowledgement
This work was supported in part by Zhejiang Province Natural Science Foundation (No. LQ21F020020), National Key Research and Development Program of China (No. 2018AAA0101900), National Natural Science Foundation of China (No. 62006207), and the Fundamental Research Funds for the Central Universities.
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Ziyu, Z., Kuang, K., Wu, F. (2021). Estimating Treatment Effect via Differentiated Confounder Matching. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_58
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DOI: https://doi.org/10.1007/978-3-030-93046-2_58
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