A Survey on Causal Inference

Abstract

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well known causal inference framework. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.

References in corpus (21)

• Sequence to Sequence Learning with Neural Networks
• Matching Methods for Causal Inference: A Review and a Look Forward
• Recursive Partitioning for Heterogeneous Causal Effects
• Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
• Homophily and Contagion Are Generically Confounded in Observational Social Network Studies
• Doubly Robust Policy Evaluation and Learning
• Causal Effect Inference with Deep Latent-Variable Models
• Recommendations as Treatments: Debiasing Learning and Evaluation
• Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
• Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
• Deep Counterfactual Networks with Propensity-Dropout
• On a Class of Bias-Amplifying Variables that Endanger Effect Estimates
• Learning Weighted Representations for Generalization Across Designs
• Overlap in Observational Studies with High-Dimensional Covariates
• Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
• Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017
• Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
• Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
• Learning Individual Causal Effects from Networked Observational Data
• Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
• Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data

Cited by in corpus (10)

• Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
• Learning Decomposed Representation for Counterfactual Inference
• Poincare: Recommending Publication Venues via Treatment Effect Estimation
• From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework
• Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
• CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation
• Improving Weakly-supervised Object Localization via Causal Intervention
• Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication
• Clinically Relevant Mediation Analysis using Controlled Indirect Effect
• Functional Data Analysis with Causation in Observational Studies: Covariate Balancing Functional Propensity Score for Functional Treatments