A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation (2505.11444v1)

Abstract: Estimating individualized treatment effects from observational data is a central challenge in causal inference, largely due to covariate imbalance and confounding bias from non-randomized treatment assignment. While inverse probability weighting (IPW) is a well-established solution to this problem, its integration into modern deep learning frameworks remains limited. In this work, we propose Importance-Weighted Diffusion Distillation (IWDD), a novel generative framework that combines the pretraining of diffusion models with importance-weighted score distillation to enable accurate and fast causal estimation-including potential outcome prediction and treatment effect estimation. We demonstrate how IPW can be naturally incorporated into the distillation of pretrained diffusion models, and further introduce a randomization-based adjustment that eliminates the need to compute IPW explicitly-thereby simplifying computation and, more importantly, provably reducing the variance of gradient estimates. Empirical results show that IWDD achieves state-of-the-art out-of-sample prediction performance, with the highest win rates compared to other baselines, significantly improving causal estimation and supporting the development of individualized treatment strategies. We will release our PyTorch code for reproducibility and future research.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation

The paper introduces a novel framework called Importance-Weighted Diffusion Distillation (IWDD) aimed at improving causal estimation from observational data. Causal inference, particularly for individualized treatment effects, is inherently challenging due to the covariate imbalance and confounding bias stemming from non-randomized treatment assignments. Conventional solutions like inverse probability weighting (IPW) provide some relief, yet their integration with deep learning frameworks has been limited. The proposed IWDD method leverages pretraining of diffusion models combined with importance-weighted score distillation, striving to offer accurate and efficient causal estimation.

Methodology

IWDD operates by first pretraining a covariate- and treatment-conditional diffusion model using the available observational data. This step allows the model to effectively capture the in-sample distribution. Subsequently, the model undergoes a distillation process where IPW is incorporated, targeting the production of a reliable conditional generator capable of addressing confounding and covariate imbalance, thereby enabling robust out-of-sample predictions.

The novel aspect of IWDD is its ability to incorporate IPW into the distillation process without explicit computation. Through a randomization-based adjustment, it mitigates the variance of gradient estimates, promising computational stability and reduced approximation bias. This adjustment involves shuffling covariates and independently sampling treatments as though drawn from randomization procedures akin to RCTs. This innovative mechanism preserves the marginal distributions while breaking the dependence between covariates and treatment assignments, effectively mimicking an RCT setup.

Empirical Results

Extensive empirical studies showcase IWDD’s state-of-the-art performance across multiple benchmark datasets. The robustness of IWDD is highlighted by its superior out-of-sample prediction capabilities and low gradient variance during the distillation process. These results affirm IWDD’s potential in shaping individualized treatment strategies by leveraging its efficient causal estimation approach.

Implications and Future Directions

Practically, IWDD's faster sampling speed and computational efficiency hold significant value for applications requiring rapid decision-making based on causal estimations. Theoretically, IWDD provides a robust framework that can be generalized to a broader set of causal estimation problems beyond binary treatment scenarios. Its unique combination of diffusion models and importance weighting within a distillation framework marks a significant development in the intersection of generative modeling and causal inference.

Looking forward, exploring IWDD's scalability to larger, more complex datasets and extending its application to continuous treatment scenarios and longitudinal data could be promising directions. Additionally, further empirical validation across diverse domains could solidify its practical utility and spur advancements in AI-driven causal inference.