Adapting Neural Networks for the Estimation of Treatment Effects (1906.02120v2)

Abstract: This paper addresses the use of neural networks for the estimation of treatment effects from observational data. Generally, estimation proceeds in two stages. First, we fit models for the expected outcome and the probability of treatment (propensity score) for each unit. Second, we plug these fitted models into a downstream estimator of the effect. Neural networks are a natural choice for the models in the first step. The question we address is: how can we adapt the design and training of the neural networks used in the first step in order to improve the quality of the final estimate of the treatment effect? We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects. The first is a new architecture, the Dragonnet, that exploits the sufficiency of the propensity score for estimation adjustment. The second is a regularization procedure, targeted regularization, that induces a bias towards models that have non-parametrically optimal asymptotic properties out-of-the-box. Studies on benchmark datasets for causal inference show these adaptations outperform existing methods. Code is available at github.com/claudiashi57/dragonnet.

• The paper presents Dragonnet, a novel neural network architecture that multitasks to predict outcomes and propensity scores for improved treatment effect estimation.
• The paper introduces targeted regularization by adding a regularization term to the training objective, boosting robustness and efficiency even with model misspecification.
• Empirical evaluations on semi-synthetic datasets demonstrate that the proposed methods improve estimator accuracy by reducing interference from irrelevant covariates.

Adapting Neural Networks for the Estimation of Treatment Effects

The paper "Adapting Neural Networks for the Estimation of Treatment Effects" contributes to the field of causal inference by focusing on how neural networks can be tailored to improve the estimation of causal effects from observational data. The authors propose two specific adaptations to the use of neural networks in estimating treatment effects: Dragonnet, a novel neural network architecture, and a method called targeted regularization. These adaptations aim to leverage insights from statistical literature to enhance the accuracy of causal effect estimation, particularly in the presence of confounding factors.

Neural Network Design for Treatment Effect Estimation

The approach to estimating treatment effects involves two primary stages: fitting models to predict outcomes and treatment probabilities, and subsequently using these models in a downstream estimator for treatment effects. The paper posits that neural networks are well-suited for the predictive tasks in the first stage, given their strong predictive capabilities. However, the novelty lies in improving the end-result for treatment effect estimation by refining how these networks are trained and architected.

Dragonnet Architecture

Dragonnet is introduced as an innovative architecture that specifically accommodates the task of treatment effect estimation. It is predicated on the principle of the sufficiency of the propensity score, which posits that adjusting for the propensity score (the probability of treatment given covariates) is sufficient for causal effect estimation. Dragonnet comprises a deep network that learns a representation to predict both outcomes and treatment assignments. Notably, it multitasks between predicting the outcome and the propensity score, potentially leading to more efficient use of covariate information pertinent to treatment predictions.

Targeted Regularization

The paper also presents targeted regularization, a modification during network training that aligns with non-parametric estimation theory principles. This approach involves adding a regularization term to the traditional training objective that incentivizes the network to satisfy a non-parametric estimating equation. The result aims to endow the estimator with desirable properties such as efficiency and robustness to model misspecification, achieving consistency in the treatment effect estimate even if one of the underlying models (outcome or treatment) is incorrect.

Empirical Evaluation

The proposed methods were evaluated using semi-synthetic datasets, particularly the IHDP benchmark and datasets from the ACIC 2018 competition. The results indicate that both Dragonnet and the integrated targeted regularization approach improve the estimation of treatment effects compared to existing neural network methods. Dragonnet, in particular, demonstrated the capability to manage noise from covariates irrelevant to treatment but predictive of outcomes, likely due to its architecture focusing on treatment-related covariate information.

Implications and Future Directions

Practically, these adaptations hold significant potential in domains where randomized control trials are impractical, allowing for more reliable causal conclusions from observational data. Theoretically, the proposed methods highlight the importance of refining model architectures and training procedures to align with specific statistical goals, such as causal inference.

The paper leaves open several avenues for further research. It invites exploration on the broader applicability of Dragonnet and targeted regularization across various causal estimands and potential mediation analyses. Additionally, understanding when and why these methods outperform traditional approaches could guide future theoretical and methodological innovations in causal inference using machine learning.

In conclusion, the paper makes a substantial contribution by adapting neural network methodology to the nuanced requirements of treatment effect estimation, demonstrating empirical robustness and offering insights for practical causal analysis.