Comparing Neural Network Architectures for Estimating Heterogeneous Treatment Effects

Core Concepts

Deep learning models can effectively estimate heterogeneous treatment effects by learning separate functions for the prognostic and treatment effect components of the outcome.

Abstract

The article compares three deep learning architectures for estimating heterogeneous treatment effects: The Farrell method, which uses a shared neural network to learn the prognostic and treatment effect functions. The BCF-nnet method, which trains separate neural networks for the prognostic and treatment effect functions. A "naive" approach that fits separate models for the treatment and control groups. The key differences are: The Farrell method shares weights between the prognostic and treatment effect functions, while BCF-nnet uses separate networks. BCF-nnet allows the propensity score to be incorporated as a feature in the prognostic function. Simulation results show that BCF-nnet outperforms the Farrell and naive methods when treatment effects are small relative to prognostic effects. This is likely due to the increased flexibility of the separate networks and the ability to incorporate the propensity score. The authors also apply the methods to a real-world dataset examining the effect of stress on sleep quality. The results suggest that the BCF-nnet approach provides more accurate estimates of the heterogeneous treatment effects.

Stats

The data generating process has the following key statistics: True average treatment effect (ATE): 0.20 True mean of the prognostic function α(X): 1.95 Range of the propensity function π(X): (0.11, 0.90)

Quotes

"Causal inference has gained much popularity in recent years, with interests ranging from academic, to industrial, to educational, and all in between." "When the assumption of treatment effect homogeneity is unwarranted, estimates of the average treatment effect (ATE) may be of questionable utility."

Key Insights Distilled From

Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation

by Demetrios Pa... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03130.pdf

Deeper Inquiries

How might the performance of these methods change if the treatment effect function β(X) was more complex or nonlinear

The performance of these methods could be impacted if the treatment effect function β(X) became more complex or nonlinear. In the case of a more complex or nonlinear β(X), the shared network approach might struggle to capture the intricate relationships between the covariates and the treatment effect. The shared network architecture assumes a shared set of hidden layers for both the prognostic and treatment effect functions, which may not be flexible enough to model highly nonlinear relationships effectively. On the other hand, the separate network approach allows for more flexibility in modeling complex and nonlinear relationships. By having distinct networks for the prognostic and treatment effect functions, the separate network approach can better capture the nuances and intricacies of a complex or nonlinear treatment effect function. This flexibility enables the separate network approach to potentially outperform the shared network approach in scenarios with more complex or nonlinear treatment effect functions.

What are the potential drawbacks or limitations of the separate network approach compared to the shared network approach

The separate network approach has certain drawbacks or limitations compared to the shared network approach. One limitation is the potential for increased computational complexity and training time. Having separate networks for the prognostic and treatment effect functions means training and optimizing two distinct models, which can be more computationally intensive compared to a shared network architecture. Additionally, the separate network approach may require more data to effectively train two separate models, as each network needs to learn independently from the data. This could be a limitation in scenarios where data is limited or where the dataset is small. Another drawback is the risk of overfitting or underfitting one of the networks. Since the separate networks do not share information during training, there is a possibility that one network may overfit the data while the other underfits, leading to suboptimal performance. In contrast, the shared network approach shares information between the prognostic and treatment effect functions, potentially leading to more robust and generalizable models.

How could these deep learning methods for causal inference be extended to handle time-varying treatments or longitudinal data

To handle time-varying treatments or longitudinal data, these deep learning methods for causal inference can be extended in several ways. One approach is to incorporate time-dependent covariates into the models to account for changes in the treatment effect over time. By including features that capture the temporal aspect of the data, such as treatment history or time since treatment initiation, the models can adapt to time-varying treatments. Additionally, recurrent neural networks (RNNs) or long short-term memory (LSTM) networks can be utilized to model sequential data in longitudinal studies. These architectures are well-suited for capturing temporal dependencies and can be applied to analyze how treatment effects evolve over time. Furthermore, causal inference methods can be combined with survival analysis techniques to account for censoring and time-to-event outcomes in longitudinal studies. By integrating deep learning models with time-series analysis and survival modeling, researchers can effectively analyze the impact of time-varying treatments on outcomes in longitudinal data settings.