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

Published 18 Apr 2024 in cs.LG and stat.ME

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.

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Summary

  • The paper introduces the NN-CGC model that integrates causal graph constraints into neural networks to mitigate spurious interactions.
  • The paper demonstrates enhanced treatment effect accuracy on synthetic and real-world datasets compared to baseline models.
  • The paper outlines future work to combine NN-CGC with other causal frameworks for tackling more complex data scenarios.

Innovating Treatment Effect Estimation with NN-CGC

Introduction

In the field of causal inference, accurately estimating treatment effects from observational data presents a range of challenges primarily due to the potential discrepancies between observed outcomes and hypothetical alternatives. This paper introduces the Neural Networks with Causal Graph Constraints (NN-CGC) model to enhance treatment effect estimation accuracy by integrating causal graph insights. This integration addresses biases caused by spurious variable interactions, leveraging novel constraints on treatment effect models that use machine learning techniques.

Formal Problem Addressed and Review of Related Work

The core objective addressed is the estimation of treatment effects wherein the causal effect of a treatment on an outcome is computed while controlling for a set of covariates represented within a causal graph. This task encounters an array of complexities such as the identifiability and estimation biases including spurious interactions, which can profoundly affect the accuracy of treatment effect estimation.

Historically, various models such as TARNet, Dragonnet, and BCAUSS have approached this problem through representation learning strategies that adapt machine learning models to this task, each carrying specific inductive biases. However, a persistent challenge is the occurrence of spurious interactions - interactions within the model that do not correspond to any causal mechanism and yet influence model predictions.

NN-CGC Model: Implementation and Novel Approach

The proposed NN-CGC model integrates structural information from causal graphs directly into the neural network architecture. It operationalizes this by:

  1. Identifying Spurious Interactions: Using the causal graph to identify and subsequently remove spurious interactions.
  2. Constrained Model Architecture: Developing a neural network architecture where model inputs are grouped and processed based on their causal linkage as informed by the causal graph, effectively constraining the learning process to focus on causally plausible interactions.

By applying these constraints, NN-CGC aims to preserve causally relevant interactions while filtering out those that could lead to biased or erroneous estimations.

Empirical Evaluation

The efficacy of NN-CGC is assessed using standard treatment effect estimation benchmarks like the IHDP and Jobs datasets, involving both synthetic and real-world data. Comparisons are drawn against baseline models including TARNet, Dragonnet, and BCAUSS without constraints.

  1. Synthetic Data Tests: Showcased improvements across various noise scenarios, particularly in lower noise settings where the causal relationships are more discernible.
  2. Real-World Data Application: Demonstrated robust performance enhancements on the IHDP dataset while showing comparable performance on the Jobs dataset, highlighting the potential challenges in real-world scenarios where causal graph discovery is less reliable.

Discussion and Potential Future Work

The NN-CGC framework introduces a significant advancement in treatment effect estimation by explicitly incorporating causal assumptions. Its ability to mitigate spurious interactions provides a methodologically sound approach to improving model accuracy and robustness.

Future directions could explore the integration of NN-CGC with other causal effect estimation frameworks to further enhance its adaptability and effectiveness across varied scenarios. Additionally, refining the approach to causal graph integration could help in addressing even more complex causal relationships and datasets with higher levels of noise or incomplete causal information.

Conclusion

NN-CGC represents a proficient step forward in the estimation of treatment effects through its innovative use of causal graph constraints within neural network architectures. The model not only sets a new benchmark in treatment effect estimation accuracy but also opens avenues for the incorporation of more explicit causal reasoning within machine learning models for enhanced inferential capabilities.

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