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CETransformer: Casual Effect Estimation via Transformer Based Representation Learning
Zhenyu Guo, Shuai Zheng, Zhizhe Liu, Kun Yan, Zhenfeng Zhu
TL;DR
Data-driven causal effect estimation faces treatment selection bias and missing counterfactual outcomes, while existing balanced-representation methods rely on hand-crafted metrics that may not suit complicated distributions. CETransformer combines self-supervised transformer representations with adversarial balancing, and the paper reports that it outperforms state-of-the-art methods on three benchmark datasets.
Problem
Data-driven causal effect estimation faces treatment selection bias and missing counterfactual outcomes, and existing balanced-representation approaches rely on hand-crafted metrics that may not suit complicated distributions.
Source: §1 · p. 2, §3.3 · p. 6Method
CETransformer uses a self-supervised transformer to learn representations from covariates and an adversarial network to balance treated and control groups.
Source: §1 · pp. 2–3, §3.3 · p. 6Results
CETransformer outperforms state-of-the-art methods on three benchmark datasets.
Source: §5 · p. 10Takeaways & Limitations
The paper reports CETransformer as a competitive model for estimating individual causal effects on the evaluated benchmark datasets.
Source: §5 · p. 10Identifying individual treatment effects requires SUTVA, consistency, ignorability, and positivity.
Source: §2 · p. 3
Abstract
from arXiv · showhide
Treatment effect estimation, which refers to the estimation of causal effects and aims to measure the strength of the causal relationship, is of great importance in many fields but is a challenging problem in practice. As present, data-driven causal effect estimation faces two main challenges, i.e., selection bias and the missing of counterfactual. To address these two issues, most of the existing approaches tend to reduce the selection bias by learning a balanced representation, and then to estimate the counterfactual through the representation. However, they heavily rely on the finely hand-crafted metric functions when learning balanced representations, which generally doesn't work well for the situations where the original distribution is complicated. In this paper, we propose a CETransformer model for casual effect estimation via transformer based representation learning. To learn the representation of covariates(features) robustly, a self-supervised transformer is proposed, by which the correlation between covariates can be well exploited through self-attention mechanism. In addition, an adversarial network is adopted to balance the distribution of the treated and control groups in the representation space. Experimental results on three real-world datasets demonstrate the advantages of the proposed CETransformer, compared with the state-of-the-art treatment effect estimation methods.
1 Introduction
Data-driven causal effect estimation must address treatment selection bias and missing counterfactual outcomes. CETransformer combines transformer-based representation learning with adversarial distribution balancing to address these challenges.
1 Introduction
- Observational data pose two challenges: treatment selection bias and missing counterfactual outcomes.Treatments are not randomly assigned, and only factual outcomes are observed, although effect estimation compares outcomes under different treatments.Source: §1 · p. 2
- Existing methods learn balanced representations and predict counterfactuals, but rely on hand-crafted metrics that may not suit complicated distributions.The paper also notes that fully connected networks focus on relationships between individuals rather than covariates.Source: §1 · p. 2
- CETransformer uses a transformer to map individuals into latent representations, where self-attention exploits correlations between covariates.The paper presents the model as an approach to causal effect estimation via transformer-based representation learning.Source: §1 · pp. 2–3
- Auto-encoder self-supervision augments training data to make the transformer trainable with limited numbers of individuals.Source: §1 · p. 3
- Adversarial learning is used to balance the treated and control groups in representation space instead of relying only on a metric function.The paper frames this as addressing distribution balancing.Source: §1 · p. 3
- The paper reports experiments on three public datasets to demonstrate the method’s effectiveness.Source: §1 · p. 3
2 Preliminary & Background
The section defines treatment effects using potential outcomes and states assumptions for identifying individual treatment effects. It distinguishes observed factual outcomes from unobserved counterfactual outcomes.
2 Preliminary & Background
- For each unit, the observed outcome is the potential outcome under its assigned treatment; the other treatment’s outcome is counterfactual.Source: §2 · p. 3
- The individual treatment effect (ITE) is the difference between a unit’s potential treated and control outcomes.Source: §2 · p. 3
- The average treatment effect (ATE) is the difference between the potential treated and control outcomes.Source: §2 · p. 3
- Identifying ITE requires SUTVA, consistency, ignorability, and positivity; under these assumptions, the counterfactual can be estimated.Source: §2 · p. 3
3 Methodology
CETransformer combines transformer-based representation learning, adversarial distribution balancing, and outcome prediction. Self-supervised reconstruction is used to support transformer training when individuals are limited.
3.1 Overview of the Proposed CETransformer Model
- The framework uses a self-supervised transformer, a discriminator to shrink treated–control differences in representation space, and outcome prediction for potential outcomes.These are the three modules in the proposed framework.Source: §3.1 · p. 4
3.2 Self-supervised Transformer
- Self-attention captures dependencies among covariates, while autoencoder-based self-supervision augments training data to support transformer training with limited individuals.A decoder reconstructs the input representation, with reconstruction error measured using MSE.Source: §3.2 · pp. 5–6
3.3 Adversarial Learning for Distribution Balancing
- Adversarial learning balances treated and control representations instead of relying on a hand-crafted metric; the model adopts WGAN as its framework.WGAN minimizes an approximation of Earth Mover distance, which the paper describes as beneficial for training stability.Source: §3.3 · p. 6
3.4 Outcome Prediction
- After representation balancing, a two-branch network predicts potential outcomes from each individual’s representation and treatment, minimizing factual-outcome mean squared error.The total objective combines three functions, with α, β, and γ controlling their trade-off.Source: §3.4 · p. 7
4 Experiments
Experiments compare CETransformer with established methods on three datasets and evaluate both treatment-effect performance and representation balance. An ablation study examines the Transformer and adversarial-learning components.
4.1 Datasets & Metric
- The evaluation uses IHDP, Jobs, and Twins, with PEHE on IHDP and Twins and policy risk on Jobs.Jobs lacks ground-truth individual treatment effects; the experiments use 10 realizations for IHDP and Twins and 10 splits for Jobs.Source: §4.1 · p. 7
4.2 Competing Algorithms
- The comparison includes 12 algorithms, including regression, tree-based, and representation-learning approaches such as ACE and DKLITE.The reported competing methods include OLS/LR1, OLS/LR2, BART, SITE, ACE, and DKLITE.Source: §4.2 · p. 7
4.3 Prediction Performance Results
- CETransformer outperforms all competing algorithms on each benchmark dataset in the reported in-sample and out-of-sample evaluation.Table 1 reports mean performance and standard deviation; lower is better.Source: §4.3 · p. 8, Table 1 · p. 8
- The learned treated and control representations overlap in t-SNE visualizations, while their KL divergence decreases rapidly over training iterations.The authors report that adversarial constraints wipe out differences between the treatment-group distributions in representation space.Source: §4.3 · p. 9, Figure 3 · p. 9, Figure 4 · p. 10
4.4 Ablation Study
- Removing the Transformer causes performance degradation, while removing adversarial learning yields performance similar to traditional supervised learning.The authors motivate the Transformer by its self-attention over feature correlations and report that imbalance should remain without adversarial learning.Source: Table 2 · p. 8, §4.4 · pp. 9–10
5 Conclusion
The paper proposes a Transformer-based balanced-representation model for individual causal-effect estimation. Experiments on three benchmark datasets show that it outperforms state-of-the-art methods, and ablations examine its components.
5 Conclusion
- CETransformer uses Transformer feature representations and adversarial learning to balance treated and control groups, and outperforms state-of-the-art methods on three benchmark datasets.The paper also reports that ablation studies demonstrate the effectiveness of its components.Source: §5 · p. 10