# Cycle-Balanced Representation Learning For Counterfactual Inference

arXiv: https://arxiv.org/abs/2110.15484 · HTML: https://mlsanity.com/paper/2110.15484/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes](https://mlsanity.com/paper/1704.02801/) (2017)
- [Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data](https://mlsanity.com/paper/1904.13335/) (2019)
- [Distributionally Robust Semi-Supervised Learning for People-Centric Sensing](https://mlsanity.com/paper/1811.05299/) (2018)

## Built on by (most cited first, 7 of 7)

- [Deep Causal Learning: Representation, Discovery and Inference](https://mlsanity.com/paper/2211.03374/) (2022)
- [Emerging Synergies in Causality and Deep Generative Models: A Survey](https://mlsanity.com/paper/2301.12351/) (2023)
- [Self-Distilled Disentangled Learning for Counterfactual Prediction](https://mlsanity.com/paper/2406.05855/) (2024)
- [Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects](https://mlsanity.com/paper/2208.06748/) (2022)
- [Advancing Counterfactual Inference through Nonlinear Quantile Regression](https://mlsanity.com/paper/2306.05751/) (2023)
- [C-XGBoost: A tree boosting model for causal effect estimation](https://mlsanity.com/paper/2404.00751/) (2024)
- [Proximal Causal Learning under Unmeasured Confounding](https://mlsanity.com/paper/2610.04519/) (2026)

## Abstract (arXiv)

With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e.g., health care and computational advertising) without Randomized Controlled Trials(RCTs). However, observational data suffer from inherent missing counterfactual outcomes, and distribution discrepancy between treatment and control groups due to behaviour preference. Motivated by recent advances of representation learning in the field of domain adaptation, we propose a novel framework based on Cycle-Balanced REpresentation learning for counterfactual inference (CBRE), to solve above problems. Specifically, we realize a robust balanced representation for different groups using adversarial training, and meanwhile construct an information loop, such that preserve original data properties cyclically, which reduces information loss when transforming data into latent representation space.Experimental results on three real-world datasets demonstrate that CBRE matches/outperforms the state-of-the-art methods, and it has a great potential to be applied to counterfactual inference.