Individual causal effect via ridge fusion

Jiaxin Pu; Tingting Ren; Shuqi Wu; Yaoxia Jiang

doi:10.1117/12.3026149

8 April 2024 Individual causal effect via ridge fusion

Jiaxin Pu, Tingting Ren, Shuqi Wu, Yaoxia Jiang

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130903V (2024) https://doi.org/10.1117/12.3026149
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Individual causal effect (ICE) plays an increasingly important role in personalized medicine and government decision-making. However, previous ICE focuses on the conditional treatment effect and the individual characteristic is embodied by the features, which makes it impossible to obtain the individual information the features are unable to include. To address this issue, we propose a heterogeneous regression model that includes a fixed individual effect and develop a ridge-fused penalty-based criterion to estimate the individual parameters. By deriving the asymptotic distribution of the estimator of individual causal effect, we make an effect individual inference for ICE. Simulation studies pose supportive evidence that the proposed unified structure of individual analysis including estimation, and inference performs well with finite samples, and a real data example is provided to illustrate the flexibility of ICE.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiaxin Pu, Tingting Ren, Shuqi Wu, and Yaoxia Jiang "Individual causal effect via ridge fusion", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130903V (8 April 2024); https://doi.org/10.1117/12.3026149

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