MultiMBNN: Matched and Balanced Causal Inference with Neural Networks

2020/04/28 by Ankit Sharma, Sharma, Ankit, Garima Gupta +9
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Health Systems, Economic Evaluations, Quality of Life #Machine Learning (cs.LG) #Methodology (stat.ME) #Multiagent Systems (cs.MA) #Statistical Methods in Clinical Trials

paper · pdf · doi:10.48550/arxiv.2004.13446

openalex publication_date 2020/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Causal inference (CI) in observational studies has received a lot of attention in healthcare, education, ad attribution, policy evaluation, etc. Confounding is a typical hazard, where the context affects both, the treatment assignment and response. In a multiple treatment scenario, we propose the neural network based MultiMBNN, where we overcome confounding by employing generalized propensity score based matching, and learning balanced representations. We benchmark the performance on synthetic and real-world datasets using PEHE, and mean absolute percentage error over ATE as metrics. MultiMBNN outperforms the state-of-the-art algorithms for CI such as TARNet and Perfect Match (PM).

Citations

• Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
• Causal Effect Inference with Deep Latent-Variable Models
• Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
• Why Propensity Scores Should Not Be Used for Matching
• Estimating individual treatment effect: generalization bounds and algorithms

Related

• Neural Score Matching for High-Dimensional Causal Inference
• Balance Regularized Neural Network Models for Causal Effect Estimation
• DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training
• Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
• Deep Learning for Causal Inference