DFITE: Estimation of Individual Treatment Effect Using Diffusion Model
Primary Area: causal reasoning
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Keywords: Individual Treatment Effect, Causal inference, diffusion model
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Abstract: Learning individualized treatment effects (ITE) from observational data is a challenging task due to the absence of unobserved confounders. Previous methods mostly focus on assuming the Ignorability assumption ignoring the unobserved confounders or overlooking the impact of an apriori knowledge on the generation process of the latent variable, which can be quite impractical in real-world scenarios. Motivated by the recent advances in the latent variable modeling, we propose to capture the unobserved latent space using diffusion model, and accordingly to estimate the causal effect. More concretely, we build on the reverse diffusion process for the unobserved confounders as a Markov chain conditioned on an apriori knowledge. In order to implement our model in a feasible way, we derive the variational bound in closed form. In the experiments, we compare our model with the state-of-the-art methods based on both synthetic and benchmark datasets , where we can empirically demonstrate consistent improvements of our model on $\sqrt{\epsilon_{PEHE}}$ and $\epsilon_{ATE}$, respectively
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Submission Number: 8962
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