A Semiparametric Approach to Causal Inference

• URL: http://arxiv.org/abs/2411.00950v1
• Date: Fri, 01 Nov 2024 18:03:38 GMT
• Title: A Semiparametric Approach to Causal Inference
• Authors: Archer Gong Zhang, Nancy Reid, Qiang Sun,
• Abstract summary: In causal inference, an important problem is to quantify the effects of interventions or treatments. In this paper, we employ a semiparametric density ratio model (DRM) to characterize the counterfactual distributions. Our model offers flexibility by avoiding strict parametric assumptions on the counterfactual distributions.
• Score: 2.092897805817524
• License:
• Abstract: In causal inference, an important problem is to quantify the effects of interventions or treatments. Many studies focus on estimating the mean causal effects; however, these estimands may offer limited insight since two distributions can share the same mean yet exhibit significant differences. Examining the causal effects from a distributional perspective provides a more thorough understanding. In this paper, we employ a semiparametric density ratio model (DRM) to characterize the counterfactual distributions, introducing a framework that assumes a latent structure shared by these distributions. Our model offers flexibility by avoiding strict parametric assumptions on the counterfactual distributions. Specifically, the DRM incorporates a nonparametric component that can be estimated through the method of empirical likelihood (EL), using the data from all the groups stemming from multiple interventions. Consequently, the EL-DRM framework enables inference of the counterfactual distribution functions and their functionals, facilitating direct and transparent causal inference from a distributional perspective. Numerical studies on both synthetic and real-world data validate the effectiveness of our approach.

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