The central role of the propensity score in observational studies for causal effects

1983/01/01 by Paul R. Rosenbaum, PAUL R. ROSENBAUM, Donald B. Rubin +1 · 586 citations
Mathematics · #Advanced Causal Inference Techniques #Statistical Methods and Inference #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.1093/biomet/70.1.41

Abstract

The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: (i) matched sampling on the univariate propensity score, which is a generalization of discriminant matching, (ii) multivariate adjustment by subclassification on the propensity score where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and (iii) visual representation of multivariate covariance adjustment by a two- dimensional plot.

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• CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
• Improving Survey Inference Using Administrative Records Without Releasing Individual‐Level Continuous Data
• Direct Bias-Correction Term Estimation for Propensity Scores and Average Treatment Effect Estimation
• Causal Machine Learning Analysis of Empirical Relative Biological Effectiveness (RBE) for Mandible Osteoradionecrosis in Head and Neck Cancer Radiotherapy
• Voluntary environmental regulation in developing countries: Mexico’s Clean Industry Program
• One‐to‐many propensity score matching in cohort studies
• Non-overlap Average Treatment Effect Bounds
• Intuition to Evidence: Measuring AI's True Impact on Developer Productivity
• Causal Machine Learning for Surgical Interventions
• Unbiased Learning-to-Rank with Biased Feedback
• Model Selection, Confounder Control, and Marginal Structural Models
• Effectiveness and equity impacts of traffic restriction schemes outside schools: a controlled natural experimental study
• Urban mobility and learning: analyzing the influence of commuting time on students' GPA at Politecnico di Milano
• The Blessings of Multiple Causes
• Burn now or never? Climate policy exposure and investment of fossil fuel firms
• Estimating Heterogeneous Treatment Effects With Real-World Health Data: A Scoping Review of Machine Learning Methods
• Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
• Impact of cooperatives on smallholders' commercialization behavior: evidence from Ethiopia
• Propensity score matching and complex surveys
• Estimating the effect of treatment on binary outcomes using full matching on the propensity score
• Variasi Bentuk Penguatan pada Pembelajaran Bahasa Jawa Kelas VII di SMP Se-Kecamatan Winong Kabupaten Pati
• Double propensity-score adjustment: A solution to design bias or bias due to incomplete matching
• Causal inference with measurement error in outcomes: Bias analysis and estimation methods
• Kernel-Distance-Based Covariate Balancing
• Balancing vs modeling approaches to weighting in practice
• Harnessing the power of virtual (digital) twins: Graphical causal tools for understanding patient and hospital differences
• Synergistic impact of marine heat waves and rapid intensification exacerbates tropical cyclone destructive power worldwide
• The Effect of an Emergency Savings Program on Employee Savings and Work Performance: A Two-Year Field Intervention
• Association of early versus late tracheostomy with prognosis in hypoxic‐ischaemic encephalopathy patients: A propensity‐matched cohort study
• Generalized Maximum Likelihood Estimators and their applications to stratified sampling and post-stratification with many unobserved strata
• The impact of prenatal alcohol exposure on sleep outcomes in 10,336 young adolescents: An Adolescent Brain Cognitive Development (ABCD) Study
• On variance estimation of the inverse probability‐of‐treatment weighting estimator: A tutorial for different types of propensity score weights
• A Propensity Score Method for Investigating Differential Item Functioning in Performance Assessment
• Nonparametric bootstrap for propensity score matching estimators
• Revisiting the impact of decoupled subsidies on farm performance: a counterfactual analysis using microdata
• To Explain or to Predict?
• The mental well-being of involuntary celibates
• Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation
• Assessing the impact of public innovation support on SMEs productivity: the critical role of regional disparities
• A Statistical Functional Approach to Estimating Common Treatment Effects in Causal Inference
• Risk-adjusted policy learning and the social cost of uncertainty: Theory and evidence from CAP evaluation
• Doubly robust matching estimators for high dimensional confounding\n adjustment
• Estimating causal effects of rare treatments on binary outcomes: addressing sample size requirements and bias correction
• Nonparametric ANCOVA for longitudinal outcomes in a randomized clinical trial
• COVID-19 emergency cash transfer and pregnancy in Brazil
• How to Measure Your App: A Couple of Pitfalls and Remedies in Measuring App Performance in Online Controlled Experiments
• SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING
• TRACE: Early Detection of Chronic Kidney Disease Onset with Transformer-Enhanced Feature Embedding
• The Power and Limits of Predictive Approaches to Observational-Data-Driven Optimization
• Engagement by Design
• Stratified Analysis of `Probabilities of Causation'
• Sampling-based vs. Design-based Uncertainty in Regression Analysis
• Estimating Semi-parametric Panel Multinomial Choice Models using Cyclic Monotonicity
• An Econometric Analysis of the Impact of Telecare on the Length of Stay in Hospital
• Three Methods for Training on Bandit Feedback
• Robust and Stable Black Box Explanations
• Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq
• Interpretable Clinical Classification with Kolmogorov-Arnold Networks
• A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications
• Rate doubly robust estimation for weighted average treatment effects
• Event Causality Identification with Synthetic Control
• Transporting Predictions via Double Machine Learning: Predicting Partially Unobserved Students' Outcomes
• NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
• Estimating quantile-specific rental yields for residential housing in Sydney
• Why Attentions May Not Be Interpretable?
• Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck
• Does Telemedicine Reduce Emergency Room Congestion? Evidence from New York State
• Content Growth and Attention Contagion in Information Networks: Addressing Information Poverty on Wikipedia
• Outlier-Resistant Heterogeneous Treatment Effect Estimation in HDLSS Settings via GAT--CVAE Framework
• Multiple imputation: a primer
• Simple Rules to Guide Expert Classifications
• Affordable Uplift: Supervised Randomization in Controlled Experiments
• Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint
• A Simple Method for Detecting Interactions between a Treatment and a Large Number of Covariates
• Transportability of Outcome Measurement Error Correction: from\n Validation Studies to Intervention Trials
• The impact of credit on productivity of smallholder farmers in Ghana
• Using Embeddings to Correct for Unobserved Confounding in Networks
• Is there life after product quality failures? Evidence from employment decisions
• Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inference
• Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies
• Deep Proxy Causal Learning and its Application to Confounded Bandit\n Policy Evaluation
• Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
• The impact of carbon emission trading schemes on urban-rural income inequality in China: A multi-period difference-in-differences method
• Learning Representations for Counterfactual Inference
• Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
• Sufficient Representations for Categorical Variables
• Treatment Effects Estimation by Uniform Transformer
• OLS and 2SLS in Randomized and Conditionally Randomized Experiments
• Prophit: Causal inverse classification for multiple continuously valued treatment policies
• Using Mobile Devices to Augment Inquiry-Based Learning Processes with Multiple Representations
• Propensity score-based estimators with multiple error-prone covariates
• Generalizing Clinical Trials with Convex Hulls
• Partially Functional Dynamic Backdoor Diffusion-based Causal Model
• Multiply Robust Inference of Average Treatment Effects by High-dimensional Empirical Likelihood
• The Economic Burden of Adults With Major Depressive Disorder in the United States (2005 and 2010)
• Generalized full matching and extrapolation of the results from a large-scale voter mobilization experiment
• Treatment effects at the margin: Everyone is marginal
• Identification of Causal Effects Within Principal Strata Using Auxiliary Variables
• Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations
• Stable Prediction across Unknown Environments
• Double/Debiased Machine Learning for Treatment and Causal Parameters
• Dual Learning Algorithm for Delayed Conversions
• A Twin Neural Model for Uplift
• Addressing Personalized Bias for Unbiased Learning to Rank
• When Is Causal Inference Possible? A Statistical Test for Unmeasured Confounding
• Covariate-adjusted win statistics in randomized clinical trials with ordinal outcomes
• Estimating heterogeneous wildfire effects using synthetic controls and\n satellite remote sensing
• Local Linear Forests
• Improving Probabilistic Record Linkage Using Statistical Prediction Models
• MOCHA: Discovering Multi-Order Dynamic Causality in Temporal Point Processes
• Causal inference in randomized clinical trials
• An Analytical Approach to Privacy and Performance Trade-Offs in Healthcare Data Sharing
• Matching-Based Nonparametric Estimation of Group Average Treatment Effects
• Data Fusion for High-Resolution Estimation
• Interpretable Almost Matching Exactly for Causal Inference
• To do or not to do: cost-sensitive causal decision-making
• Treatment Allocation under Uncertain Costs
• Learning from Trade through Innovation
• Forest-based livelihoods, income, and poverty: Empirical evidence from the Himalayan region of rural Pakistan
• Statistical Matching of the German Aging Survey and the Sample of Active Pension Accounts as a Source for Analyzing Life Courses and Old Age Incomes
• Time-varying confounding in epidemic intervention evaluations
• Vitamin N: Benefits of Different Forms of Public Greenery for Urban Health
• Immigration and Recidivism: What Is the Link?
• Likelihood confidence intervals for misspecified Cox models
• Evaluation Methods for Non‐Experimental Data
• Econometrics for Evaluations: An Introduction to Recent Developments
• Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests
• Confounding is a Pervasive Problem in Real World Recommender Systems
• Variance Estimation for Weighted Average Treatment Effects
• A hierarchical modelling approach for Bayesian Causal Forests on longitudinal data: A Case Study in Multiple Sclerosis Clinical Trials
• Causal World Models by Unsupervised Deconfounding of Physical Dynamics
• Immunosuppressed patients with COVID-19 pneumonia in ICU: clinical characteristics and factors influencing outcomes
• Estimating causal effects from panel data with dynamic multivariate panel models
• A framework for causal estimand selection under positivity violations
• Hospital Readmission After Traumatic Brain Injury Hospitalization in Community‐Dwelling Older Adults
• From exposure to preference: How solar electrification experiences shape long-term sustainability in rural Pakistan
• “How is your thesis going?”–Ph.D. students’ perspectives on mental health and stress in academia
• Holistic Explainable AI (H-XAI): Extending Transparency Beyond Developers in AI-Driven Decision Making
• DFW: A Novel Weighting Scheme for Covariate Balancing and Treatment Effect Estimation
• Minimax Rates and Adaptivity in Combining Experimental and Observational Data
• Nutritional impact of excluding red meat from the Canadian diet
• The impacts of widespread agricultural interventions on yields and food security in Ethiopia☆
• Adaptive Data-Borrowing for Improving Treatment Effect Estimation using External Controls
• Sensitivity of weighted least squares estimators to omitted variables
• Case Selection Techniques in Case Study Research
• Structure Maintained Representation Learning Neural Network for Causal Inference
• Harnessing the Power of Interleaving and Counterfactual Evaluation for Airbnb Search Ranking
• A Comparative Evaluation of Statistical Methods in Hybrid Controlled Trials
• Reinforcement Learning for Uplift Modeling
• Active Learning with Logged Data
• Horseshoe Forests for High-Dimensional Causal Survival Analysis
• Impact of Land Subsidence on Housing Sale Values
• Health care costs in the last week of life: associations with end-of-life conversations. [europepmc]
• An introduction to causal inference. [europepmc]
• Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. [europepmc]
• Type I error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses. [europepmc]
• The effect on survival of continuing chemotherapy to near death. [europepmc]
• Targeted maximum likelihood based causal inference: Part I. [europepmc]
• Children's antisocial behavior, mental health, drug use, and educational performance after parental incarceration: a systematic review and meta-analysis. [europepmc]
• Evaluation of the propensity score methods for estimating marginal odds ratios in case of small sample size. [europepmc]
• Simpson's paradox in psychological science: a practical guide. [europepmc]
• Prescription opioid analgesics increase the risk of depression. [europepmc]
• The Army study to assess risk and resilience in servicemembers (Army STARRS). [europepmc]
• Making a Difference in Science Education: The Impact of Undergraduate Research Programs. [europepmc]
• Big data analysis of treatment patterns and outcomes among elderly acute myeloid leukemia patients in the United States. [europepmc]
• Impact of unintended pregnancy on maternal mental health: a causal analysis using follow up data of the Panel Study on Korean Children (PSKC). [europepmc]
• Efficacy of group social skills interventions for youth with autism spectrum disorder: A systematic review and meta-analysis. [europepmc]
• Matching Weights to Simultaneously Compare Three Treatment Groups: Comparison to Three-way Matching. [europepmc]
• Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance. [europepmc]
• Examining Plausibility of Self-Reported Energy Intake Data: Considerations for Method Selection. [europepmc]
• Network-based approach to prediction and population-based validation of in silico drug repurposing. [europepmc]
• Association of Patient Priorities-Aligned Decision-Making With Patient Outcomes and Ambulatory Health Care Burden Among Older Adults With Multiple Chronic Conditions: A Nonrandomized Clinical Trial. [europepmc]
• Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances. [europepmc]
• Latent Class Analysis for Developmental Research. [europepmc]
• Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. [europepmc]
• Propensity score matching with R: conventional methods and new features. [europepmc]
• Participation bias in the UK Biobank distorts genetic associations and downstream analyses. [europepmc]

Related

• An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
• Invited Commentary: Propensity Scores
• Propensity Score Adapted Covariate Selection for Causal Inference
• Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
• Propensity Score Matching underestimates Real Treatment Effect, in a simulated theoretical multivariate model