Causal Contrastive Learning for Counterfactual Regression Over Time

• URL: http://arxiv.org/abs/2406.00535v3
• Date: Tue, 29 Oct 2024 00:12:20 GMT
• Title: Causal Contrastive Learning for Counterfactual Regression Over Time
• Authors: Mouad El Bouchattaoui, Myriam Tami, Benoit Lepetit, Paul-Henry Cournède,
• Abstract summary: This paper introduces a unique approach to counterfactual regression over time, emphasizing long-term predictions. Distinguishing itself from existing models like Causal Transformer, our approach highlights the efficacy of employing RNNs for long-term forecasting. Our method achieves state-of-the-art counterfactual estimation results using both synthetic and real-world data.
• Score: 3.3523758554338734
• License:
• Abstract: Estimating treatment effects over time holds significance in various domains, including precision medicine, epidemiology, economy, and marketing. This paper introduces a unique approach to counterfactual regression over time, emphasizing long-term predictions. Distinguishing itself from existing models like Causal Transformer, our approach highlights the efficacy of employing RNNs for long-term forecasting, complemented by Contrastive Predictive Coding (CPC) and Information Maximization (InfoMax). Emphasizing efficiency, we avoid the need for computationally expensive transformers. Leveraging CPC, our method captures long-term dependencies in the presence of time-varying confounders. Notably, recent models have disregarded the importance of invertible representation, compromising identification assumptions. To remedy this, we employ the InfoMax principle, maximizing a lower bound of mutual information between sequence data and its representation. Our method achieves state-of-the-art counterfactual estimation results using both synthetic and real-world data, marking the pioneering incorporation of Contrastive Predictive Encoding in causal inference.

Related papers

• Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI [12.569286058146343]
  We establish a formal connection between the decades-old surrogate outcome model in biostatistics and the emerging field of prediction-powered inference (PPI) We develop recalibrated prediction-powered inference, a more efficient approach to statistical inference than existing PPI proposals. We demonstrate significant gains in effective sample size over existing PPI proposals via three applications leveraging state-of-the-art machine learning/AI models.
  arXiv  Detail & Related papers  (2025-01-16T18:30:33Z)
• Estimating the treatment effect over time under general interference through deep learner integrated TMLE [7.2615408834692685]
  We introduce DeepNetTMLE, a deep-learning-enhanced Targeted Maximum Likelihood Estimation (TMLE) method. DeepNetTMLE mitigates bias from time-varying confounders under general interference. We show that DeepNetTMLE achieves lower bias and more precise confidence intervals in counterfactual estimates.
  arXiv  Detail & Related papers  (2024-12-06T06:09:43Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning [35.119957381211236]
  We introduce Counterfactual Self-Supervised Transformer (COSTAR), a novel approach that integrates self-supervised learning for improved historical representations. COSTAR yields superior performance in estimation accuracy and generalization to out-of-distribution data compared to existing models.
  arXiv  Detail & Related papers  (2023-11-01T22:38:14Z)
• PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection [65.24854366973794]
  Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce. We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD. Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
  arXiv  Detail & Related papers  (2023-10-18T02:59:57Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [53.37679435230207]
  We propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data.
  arXiv  Detail & Related papers  (2022-09-23T16:13:47Z)
• Causal Transformer for Estimating Counterfactual Outcomes [18.640006398066188]
  Estimating counterfactual outcomes over time from observational data is relevant for many applications. We develop a novel Causal Transformer for estimating counterfactual outcomes over time. Our model is specifically designed to capture complex, long-range dependencies among time-varying confounders.
  arXiv  Detail & Related papers  (2022-04-14T22:40:09Z)
• Towards Handling Uncertainty-at-Source in AI -- A Review and Next Steps for Interval Regression [6.166295570030645]
  This paper focuses on linear regression for interval-valued data as a recent growth area. We conduct an in-depth analysis of state-of-the-art methods, elucidating their behaviour, advantages, and pitfalls when applied to datasets with different properties.
  arXiv  Detail & Related papers  (2021-04-15T05:31:10Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.