Causal Effect Inference with Deep Latent-Variable Models (1705.08821v2)

Abstract: Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We build on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect. Our method is based on Variational Autoencoders (VAE) which follow the causal structure of inference with proxies. We show our method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.

• The paper introduces CEVAE, which leverages deep latent-variable models to infer unobserved confounders and estimate individual treatment effects.
• It embeds Variational Autoencoders within a causal graph framework to enhance accuracy in treatment effect estimation on benchmarks like IHDP.
• Numerical results demonstrate CEVAE’s resilience to noise in proxy variables, ensuring robust performance in real-world causal inference scenarios.

Causal Effect Inference with Deep Latent-Variable Models: An Expert Synopsis

Overview

The paper explores the challenge of estimating individual-level causal effects from observational data using deep latent-variable models. The primary focus lies on handling the issue of confounders, particularly when these confounders are not directly measurable but can be inferred from proxy variables. By leveraging recent advancements in Variational Autoencoders (VAEs), the authors aim to simultaneously estimate the latent space representing confounders and the causal effect itself.

Methodology

The methodology builds upon the causal graph framework, embedding it into a Variational Autoencoder structure. This approach models the latent confounders and treats their proxies with explicit representation, which aligns with the causal inference process. The proposed model, termed as the Causal Effect Variational Autoencoder (CEVAE), consists of:

• Generative Model: Represents the probabilistic relationships in the data, considering the treatment assignment and outcomes conditioned on the latent confounder.
• Inference Network: Utilizes neural networks to approximate the posterior distribution of the latent variables.

CEVAE's architecture incorporates functions to capture the intrinsic non-linear and complex interactions present in causal relationships, thus providing a comprehensive method to evaluate individual treatment effects (ITE) and average treatment effects (ATE).

Numerical Results

The paper demonstrates the robustness of the CEVAE model against hidden confounding through various synthetic and real-world datasets, such as the IHDP benchmark and twin births dataset. Key findings include:

• CEVAE's competitive performance on the IHDP benchmark, indicating reasonable accuracy in estimating treatment effects compared to existing methods.
• Enhanced resilience to noise in proxy variables, showcasing the model's ability to maintain prediction accuracy even with imperfect data representations.
• On the Twins dataset, CEVAE consistently showed lower error rates in counterfactual inference and ATE estimation under increasing noise levels, highlighting its robustness where proxy variables are unreliable.

Implications and Future Directions

The integration of VAEs into causal inference frameworks provides a promising pathway for handling latent confounding in complex datasets. The flexibility of neural architectures allows CEVAE to accommodate various data intricacies without stringent assumptions on the data-generating process. However, it emphasizes a trade-off in theoretical identifiability for empirical performance, characteristic of deep learning models.

Future research may aim to refine theoretical guarantees associated with VAEs in causal inference, explore applications across different domains with inherently high-dimensional data, and address related challenges such as selection bias. The potential to further couple these models with advances in tensor decomposition and other unsupervised learning techniques could revolutionize causal effect inference, especially in scenarios rife with unobserved confounders.