Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation (2404.12238v1)

Abstract: In recent years, there has been a growing interest in using machine learning techniques for the estimation of treatment effects. Most of the best-performing methods rely on representation learning strategies that encourage shared behavior among potential outcomes to increase the precision of treatment effect estimates. In this paper we discuss and classify these models in terms of their algorithmic inductive biases and present a new model, NN-CGC, that considers additional information from the causal graph. NN-CGC tackles bias resulting from spurious variable interactions by implementing novel constraints on models, and it can be integrated with other representation learning methods. We test the effectiveness of our method using three different base models on common benchmarks. Our results indicate that our model constraints lead to significant improvements, achieving new state-of-the-art results in treatment effects estimation. We also show that our method is robust to imperfect causal graphs and that using partial causal information is preferable to ignoring it.

• The paper introduces NN-CGC, integrating causal graph constraints into neural networks to enhance treatment effects estimation.
• It addresses biases from spurious variable interactions by enforcing adjustments that meet the backdoor criterion.
• Empirical evaluations on synthetic, semi-synthetic, and real datasets demonstrate improved precision relative to traditional models.

Summary of "Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation"

The paper "Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation" presents a method for integrating causal graph constraints into neural networks to improve treatment effects estimation. This approach, known as NN-CGC, aims to address biases resulting from spurious variable interactions that can distort causal inference models.

Introduction

Causal inference seeks to determine how specific actions influence outcomes, a task complicated by observational data constraints, where counterfactual scenarios are inherently unobservable. The paper addresses these challenges by leveraging neural networks with constraints derived from causal graphs to estimate treatment effects more accurately. NN-CGC introduces constraints that capitalize on causal information, reducing the influence of spurious interactions without requiring comprehensive knowledge of causal graphs.

Causal Inference and Bias

A key challenge in causal inference is identifying valid estimands, which NN-CGC addresses by enforcing constraints consistent with causal graphs. The process involves creating adjustment sets that satisfy the backdoor criterion, facilitating the isolation of causal impacts while mitigating biases such as exchangeability, positivity, consistency, and spurious interactions. NN-CGC uses causal graph information to define variable groups, allowing the neural network to learn distributions aligned with the causal model.

Figure 1: In the causal inference workflow (A), after identification, we are left with a statistical estimand. The process from here onwards is similar to the prediction workflow (B) but the statistical estimands are different.

Methodology and Implementation

NN-CGC enhances existing representation-based learners like TARNet, Dragonnet, and BCAUSS by introducing novel constraints in the neural network architecture's pre-representation phase. The model's input layer is divided into groups based on causal information, ensuring that only causally valid interactions inform the learned representation. Post-representation layers remain unchanged, allowing NN-CGC to be compatible with state-of-the-art methods.

Neural Network Architecture

The neural network architecture used by NN-CGC restricts variable interactions to those defined by causal graph constraints. As depicted in Figure 2, the architecture comprises independent layers for each variable group identified from the causal graph, ensuring interactions within these groups remain causally valid. The output layers can leverage any existing architecture, such as TARNet or Dragonnet. This configuration aims to reduce spurious interactions while enhancing the model's robustness to imperfect causal graphs.

Figure 2: Model architecture when applying CGC to the Dragonnet. The post-representation part remains identical, but the pre-representation layers are divided according to the groups of variables.

Empirical Evaluation

Empirical testing illustrates NN-CGC's effectiveness across both synthetic and semi-synthetic datasets, such as IHDP and Jobs. Results indicate NN-CGC consistently improves treatment effect estimation by achieving lower error metrics in scenarios with clearly defined causal graphs.

Synthetic Experiments

In synthetic datasets, NN-CGC models demonstrated superior performance over unconstrained models across various noise levels. However, in high-noise scenarios, distinguishing spurious from valid interactions becomes challenging, potentially affecting NN-CGC's advantages. Despite these challenges, NN-CGC models generally outperformed conventional models in estimating individual treatment effects.

Semi-synthetic and Real Data

For the IHDP dataset, NN-CGC surpassed competing methods, setting new benchmarks for precision in treatment effect estimation. The Jobs dataset further validated NN-CGC's practical applicability, showing competitive performance despite variations in causal graph discovery reliability.

Conclusion

The research introduces NN-CGC as a promising approach for treatment effect estimation in causal inference tasks. By integrating causal graph constraints into neural network architectures, NN-CGC effectively reduces the impact of spurious interactions, leading to more accurate and reliable causal effect estimations. Future work may explore enhancing NN-CGC further by developing graphical conditioning methods or optimizing group weight sharing to maximize data efficiency.