# Deep Counterfactual Networks with Propensity-Dropout

arXiv: https://arxiv.org/abs/1706.05966 · HTML: https://mlsanity.com/paper/1706.05966/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2017.

## Built on by (most cited first, 30 of 30)

- [On Inductive Biases for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2106.03765/) (2021)
- [Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2202.01336/) (2022)
- [Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation](https://mlsanity.com/paper/2210.06183/) (2022)
- [Estimating Categorical Counterfactuals via Deep Twin Networks](https://mlsanity.com/paper/2109.01904/) (2021)
- [Towards Causal Foundation Model: on Duality between Causal Inference and Attention](https://mlsanity.com/paper/2310.00809/) (2023)
- [Adversarial Counterfactual Environment Model Learning](https://mlsanity.com/paper/2206.04890/) (2022)
- [Deep Causal Learning: Representation, Discovery and Inference](https://mlsanity.com/paper/2211.03374/) (2022)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [Deep Learning for Time Series Forecasting: A Survey](https://mlsanity.com/paper/2503.10198/) (2025)
- [Estimating Potential Outcome Distributions with Collaborating Causal Networks](https://mlsanity.com/paper/2110.01664/) (2021)
- [Estimating Causal Effects using a Multi-task Deep Ensemble](https://mlsanity.com/paper/2301.11351/) (2023)
- [Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation](https://mlsanity.com/paper/2203.03185/) (2022)
- [Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge](https://mlsanity.com/paper/2102.06271/) (2021)
- [Is More Data All You Need? A Causal Exploration](https://mlsanity.com/paper/2206.02409/) (2022)
- [Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation](https://mlsanity.com/paper/2307.03315/) (2023)
- [Causal Inference with Complex Treatments: A Survey](https://mlsanity.com/paper/2407.14022/) (2024)
- [Learning Matching Representations for Individualized Organ Transplantation Allocation](https://mlsanity.com/paper/2101.11769/) (2021)
- [DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data](https://mlsanity.com/paper/2303.04201/) (2023)
- [Ads Supply Personalization via Doubly Robust Learning](https://mlsanity.com/paper/2410.12799/) (2024)
- [The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted L1 regularized Neural Networks Predictions](https://mlsanity.com/paper/2108.00990/) (2021)
- [A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations](https://mlsanity.com/paper/2311.00923/) (2023)
- [Causal Machine Learning for Surgical Interventions](https://mlsanity.com/paper/2509.19705/) (2025)
- [Non parametric estimation of causal populations in a counterfactual scenario](https://mlsanity.com/paper/2112.04288/) (2021)
- [TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective](https://mlsanity.com/paper/2212.08890/) (2022)
- [VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference](https://mlsanity.com/paper/2308.00904/) (2023)
- [Enhancing predictive imaging biomarker discovery through treatment effect analysis](https://mlsanity.com/paper/2406.02534/) (2024)
- [MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction](https://mlsanity.com/paper/2407.14065/) (2024)
- [Are causal effect estimations enough for optimal recommendations under multitreatment scenarios?](https://mlsanity.com/paper/2410.05177/) (2024)
- [Consistent Causal Inference of Group Effects in Non-Targeted Trials with Finitely Many Effect Levels](https://mlsanity.com/paper/2504.15854/) (2025)
- [Structure Maintained Representation Learning Neural Network for Causal Inference](https://mlsanity.com/paper/2508.01865/) (2025)

## Abstract (arXiv)

We propose a novel approach for inferring the individualized causal effects of a treatment (intervention) from observational data. Our approach conceptualizes causal inference as a multitask learning problem; we model a subject's potential outcomes using a deep multitask network with a set of shared layers among the factual and counterfactual outcomes, and a set of outcome-specific layers. The impact of selection bias in the observational data is alleviated via a propensity-dropout regularization scheme, in which the network is thinned for every training example via a dropout probability that depends on the associated propensity score. The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers. Experiments conducted on data based on a real-world observational study show that our algorithm outperforms the state-of-the-art.