Library · Causal inference & statistics
What Propensity Score Matching got wrong, from 57 theses
Summary written by a model from the records below: Researchers investigating propensity score matching frequently encountered severe sample attrition, persistent covariate imbalance, and vulnerability to unmeasured confounding. Across numerous settings, matching procedures underperformed parametric regression adjustments or proved methodologically incompatible with continuous treatments and longitudinal data structures. These records come from graduate theses, 2021 to 2025, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 9 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
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Sample attrition and participant loss from strict matching calipers and common support trimming
Enforcing caliper distance thresholds and trimming participants outside the region of common support discarded large fractions of study cohorts. Analysts frequently rejected matching procedures to prevent severe sample loss and selection bias resulting from unmatched observations.
Considered and rejected
Considered and rejected: Decided against HDPS propensity score matching in favor of regression with overlap trimming due to severe sample loss (30-80% in matching vs 1-5% in regression).
Use of antiseizure medications during pregnancy and adverse neonatal outcomes · University of Manitoba · PhD
Lost to a baseline
562 ACDF cases excluded due to lack of suitable CDA propensity score matches (caliper mismatch in 2:1 matching)
Comparative Outcomes in Cervical Spine Surgery: A Dual Analysis of Revision Surgeries and Adjacent Segment Degeneration · Harvard · Master's
Lost to a baseline
Chapter 2.4 (Abortion quasi-experiment): Excluded control participants lacking comparable propensity score mix as well as treated women with very high propensity scores outside common support during 1:1 nearest-neighbor matching.
Parenthood and Life Satisfaction: The Consequences of Childbirth, Alternative Pregnancy Outcomes, and Single Parenting on Well-Being in Several Domains of Life · Leibniz Universität Hannover · PhD
Lost to a baseline
4 of 24 ONJ cases (and unmatched osteoporosis controls from the pool of 874) could not be matched during propensity score matching, leaving 20 matched pairs.
Estado Cualitativo Y Cuantitativo Oseo Generalizado En La Osteonecrosis De Los Maxilares. Efectos De Los Bifosfonatos. · University of Las Palmas de Gran Canaria · PhD
Lost to a baseline
166 patients were dropped during propensity score trimming due to lacking common support/overlap (overall sample decreased from 8,495 to 8,329 matched patients: 13 in stage I–II HR+/HER2−, 4 in stage III HR+/HER2−, 42 in stage I–II HER2+, 70 in stage III HER2+, 14 in stage I–II TNBC, and 23 in stage III TNBC)
Neoadjuvant versus adjuvant chemotherapy for older adults with stage I–III breast cancer · UT Austin · PhD
Lost to a baseline
Study 1 (Manuscript #3): 2 male outlier participants excluded during propensity score matching due to age distribution constraints.
In-Group and Out-Group Perspectives on Content of Social Groups · University of Marburg · PhD
Considered and rejected
Considered and rejected: Rejected propensity score matching in favor of multivariable regression models, citing selection bias from censoring and sample size loss from unmatched subjects.
A COMPARISON OF HOSPITALIZATION OUTCOMES BETWEEN PERITONEAL DIALYSIS AND HOME HEMODIALYSIS PATIENTS IN CANADA · Dalhousie · Master's
Lost to a baseline
13 exposed individuals and 807 non-exposed individuals from the initial IBD cohort were excluded because they could not be matched using propensity score and disease duration calipers (PS caliper ±0.05, disease duration caliper ±0.5).
EVALUATING THE IMPACT OF THE SASKATCHEWAN INTEGRATED CARE FOR INFLAMMATORY BOWEL DISEASE ON DIRECT HEALTHCARE COSTS AND QUALITY OF LIFE · Saskatchewan · Master's
Lost to a baseline
278 towns trimmed from main propensity score analysis because their estimated propensity score fell outside the support of the control group (leaving 53 treated and 102 control out of 439)
Essays on the Political Economy of City Status · Duke · PhD
Considered and rejected
Considered and rejected: Decided against using propensity score matching in Paper 2 due to small sample size in subgroups and covariate imbalance causing sample attrition, using multivariable logistic regression instead.
Understanding the adverse impact of centralised care on neonatal outcomes · Nottingham · PhD
Considered and rejected
Considered and rejected: Rejected full propensity score matching in favor of propensity score covariate adjustment to avoid discarding unmatched cases in small samples.
The Role Of For-Profit Education In Social Stratification And Social Inequality In The United States · Penn · PhD
Lost to a baseline
954 treated customers dropped during 1-to-1 propensity score matching with caliper u = 0.05 (sample reduced from 8,730 to 7,776 treated customers)
ESSAYS ON DIGITAL EXPERIENCE · Cornell · PhD
Lost to a baseline
171 treated customers dropped during 1-to-1 rolling entry matching without replacement with caliper u = 0.05 (sample reduced from 977 to 806 treated customers)
ESSAYS ON DIGITAL EXPERIENCE · Cornell · PhD
Considered and rejected
Considered and rejected: Nearest-neighbor propensity score matching was rejected in favor of kernel matching due to poor common support across treatment and control groups.
Socioeconomic implications of adverse birth outcomes · Leibniz Universität Hannover · PhD
Inability to achieve covariate balance and heightened model dependence
Propensity score specifications and nearest neighbor routines frequently failed to balance baseline covariates or worsened imbalances compared to coarsened exact matching. Incorporating high dimensional empirical covariates or noise features degraded matching quality and increased dependence on subjective caliper choices.
Tried and failed
propensity score weighting on baseline covariate levels applied to correcting differential pre-treatment trends. Reason: conditioning on baseline level covariates failed to balance differential pre-treatment trajectory trends across groups
Essays in Labor and Public Economics · Cornell · PhD
Tried and failed
covariate matching with high dimensional noise features applied to experimental design treatment assignment. Outcome: worse than baseline. Reason: irrelevant features degrade match quality on important predictive covariates, reducing finite-sample precision
Essays on Experimental Design · MIT · PhD
Tried and failed
1-to-1 nearest neighbour propensity score matching applied to clustered observational data. Outcome: worse than baseline. Reason: dropping poor pairs or matching with replacement degraded covariate balance and increased model dependence
A modified t-test for treatment means in unreplicated classroom comparisons and Matching Methods for reducing data imbalance · Iowa State · PhD
Considered and rejected
Considered and rejected: Rejected 1:1 nearest neighbour and optimal propensity score matching due to covariate imbalance; used full matching with probit regression instead.
Effects of the Brain Derived Neurotrophic Factor Val66met Polymorphism on the Structural and Functional Architecture of the Human Brain · York University · Master's
Considered and rejected
Considered and rejected: Rejected propensity score matching and weighting due to inability to achieve adequate covariate balance across treatment and comparison groups, opting instead for baseline covariate regression adjustment in interrupted time series.
Universal Free School Meals: Implementation of the Community Eligibility Provision and Impacts on Student Nutrition, Behavior and Academic Performance · Johns Hopkins · PhD
Tried and failed
high-dimensional propensity score with many empirical covariates applied to observational cohort confounding adjustment. Outcome: unstable. Reason: adding over 200 empirical covariates compromised balance and created extreme inverse probability weights
Real-World Bleeding with Ibrutinib in B-Cell Malignancies · Penn · PhD
Considered and rejected
Considered and rejected: Rejected using a 0.2 standard deviation propensity score matching caliper because the resulting sample (n=2016) was not balanced across all predictor covariates.
Judgment and Decision-Making in the Context of Health and Law · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected standard propensity score matching (PSM) due to model dependence, caliper tradeoffs, and subjective bias from uneven density distributions.
Debiased machine learning causal inference for time-varying social variables · Oxford · PhD
Considered and rejected
Considered and rejected: Rejected propensity score matching in favor of coarsened exact matching due to concerns over model dependence and covariate imbalance.
Quasi-Experimental Evaluation of Women's Re-Entry in New Jersey - Through a Black Intersectional Lens · Carleton University · Master's
Considered and rejected
Considered and rejected: Rejected using Propensity Score Matching (PSM), specifically nearest neighbor, because it increased covariate imbalance compared to Coarsened Exact Matching (CEM).
The Effects of Financial and Economic Literacy on Individual Policy Preferences · University of Washington · PhD
Tried and failed
exact matching on discrete spatial temporal covariates applied to observational unit covariate matching. Outcome: worse than baseline. Reason: forcing exact discrete matches degraded overall covariate balance and increased multivariate distance across continuous attributes
Methodological advances in benefit transfer and hedonic analysis · Virginia Tech · Doctorate
Tried and failed
multi-nearest-neighbor matching with simple ATT estimator applied to causal treatment effect estimation. Outcome: worse than baseline. Reason: increasing number of matches compounded bias under poor covariate balance conditions
Modern Econometric Methods for the Analysis of Housing Markets · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Propensity score matching rejected in favor of Mahalanobis distance matching to avoid covariate imbalance and model dependence.
Educational Disparities in Chronic Pain and Life Expectancy: Gaps and Pathways · University at Buffalo · PhD
Inferior statistical precision and higher estimation bias compared to regression or machine learning baselines
Propensity score matching yielded lower sample efficiency, wider confidence intervals, and weaker statistical power than parametric regressions and doubly robust estimators. In complex observational benchmarks, matching exhibited substantially higher estimation bias than flexible machine learning alternatives like Bayesian additive regression trees.
Tried and failed
propensity score matching for differential expression applied to single-cell perturbation gene expression detection. Outcome: worse than baseline. Reason: did not outperform standard Student's t-test and Kolmogorov-Smirnov statistical tests
Assessing regulatory function of rare and common variants using expression CROP-sequencing · Georgia Tech · PhD
Tried and failed
generalized additive models applied to propensity score estimation. Outcome: worse than baseline. Reason: did not improve over logistic regression due to high proportion of dummy variables
Disruption analytics in urban metro systems with large-scale automated data · Imperial · PhD
Lost to a baseline
Logistic regression baseline had less noisy propensity score calibration than BICauseTree due to better data efficiency
Towards trustworthy AI: from local explanations to causal understanding · Oxford · PhD
Considered and rejected
Considered and rejected: Rejected using matched propensity score (PSMATCH) or univariate models alone for disease impact, selecting the doubly robust method for lower variance and narrower confidence intervals
Whole-herd drivers of wean-to-finish mortality under field conditions: a data-driven approach · Iowa State · PhD
Considered and rejected
Considered and rejected: Rejected propensity score matching (PSM) across full marker sets due to the curse of dimensionality, opting for linear probability model regression adjustment
Markers of a real estate agent's value-add · UT Austin · PhD
Lost to a baseline
Propensity score matching and prognostic score matching showed much higher estimation bias (-42.01% and -201.33%) than BART-CV (-19.50%) on LaLonde PSID-2 observational data
CAUSAL INFERENCE FOR HIGH-STAKES DECISIONS · Duke · PhD
Considered and rejected
Considered and rejected: Standard propensity score matching (PSM) was rejected/avoided in favor of IPWRA and BCM due to inconsistency and bias when matching with multiple continuous covariates.
Nutritional and environmental impacts of livestock production systems in Canada: a food systems perspective · University of Manitoba · PhD
Considered and rejected
Considered and rejected: Rejected propensity score matching for causal inference due to documented drawbacks in matching compared to inverse propensity weighting (IPW).
Causal Inference with Selection Bias and Complex Observation Data · University at Buffalo · PhD
Considered and rejected
Considered and rejected: Rejected propensity score matching in favor of Mahalanobis nearest distance matching due to known methodological pitfalls and bias.
Evaluating the impact of conservation and development interventions on Guatemalan and Colombian forest ecosystems · Boston University · PhD
Incompatibility with continuous treatments, longitudinal panel structures, and complex study designs
Propensity score matching struggled when applied to continuous treatment durations, multi group non linear distributions, or panel settings requiring within person centering. Analysts also found matching redundant alongside difference in differences models or rejected it because selecting extreme utilizers introduced regression to the mean bias.
Tried and failed
propensity score matching with difference in differences applied to observational policy impact estimation. Reason: matched estimation yielded results nearly identical to standard difference in differences without added benefit
Considered and rejected
Considered and rejected: Rejected propensity score matching in Chapter 2.3 in favor of fixed effects linear regressions with within-person centering to examine causal life-course trajectories rather than simply adjusting for pre-event differences.
Parenthood and Life Satisfaction: The Consequences of Childbirth, Alternative Pregnancy Outcomes, and Single Parenting on Well-Being in Several Domains of Life · Leibniz Universität Hannover · PhD
Tried and failed
paired t-tests for post-matching balance evaluation applied to covariate balance assessment after matching. Reason: matching reduces within-pair variance, causing smaller mean differences to yield misleadingly large t-statistics
Contributions To Multivariate Matching In Observational Studies · Penn · PhD
Considered and rejected
Considered and rejected: Rejected standard propensity score matching/univariate covariate bounds because they fail when covariate distributions and overlap are non-linear and multi-group
Tackling Key Challenges to Guide Clinical Decisions in Cardiovascular Diseases · MIT · PhD
Tried and failed
doubly robust propensity score estimation applied to observational pricing data. Outcome: no signal. Reason: treatment assignment had no correlation with observed features, breaking propensity score estimation
Considered and rejected
Considered and rejected: Rejected propensity score matching to generate control groups because DiD handles pre-intervention mean differences and matching introduces regression-to-the-mean bias when selecting extreme initial-year utilizers.
Cost-Effective Management of Diseases: Early Detection and Interventions for Improved Health Outcomes · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected re-fitting the propensity score using post-LASSO within each bootstrap resample because bootstrap is inconsistent for LASSO estimators.
Propensity Score Methods For Causal Subgroup Analysis · Duke · PhD
Considered and rejected
Considered and rejected: Rejected using Inverse Probability of Treatment Weighting (IPTW) propensity score matching because bivariate baseline demographic/clinical characteristics did not significantly differ between cohorts
Considered and rejected
Considered and rejected: Propensity score matching was rejected because it is designed for binary treatments, while maternity leave duration was continuous
Considered and rejected
Considered and rejected: Rejected using baseline LDL, HDL, and continuous HbA1C in the propensity score model due to high EHR missingness (>30-50%), replacing them with binary measurement-presence indicators
Estimation bias and variance instability from propensity model misspecification and extreme weights
Misspecifying propensity models or pairing them with misspecified outcome imputations produced substantial asymptotic bias that did not diminish with sample size. In addition, extreme estimated propensity scores and weight truncation distorted covariate balance and generated large estimator variance.
Tried and failed
complex non-linear models for propensity score estimation applied to fairness sample re-weighting under MAR. Outcome: worse than baseline. Reason: overfit binary indicators instead of accurately estimating continuous propensity probabilities
ADVANCING ROBUST AND FAIR STATISTICAL AND MACHINE LEARNING MODELS FOR INCOMPLETE DATA · Penn · PhD
Tried and failed
standard logistic regression for propensity score estimation applied to clustered data with misclassified dropouts. Reason: misclassification of cluster-level missingness induces substantial estimation bias under small cluster sizes
Statistical Methods for Addressing Missing Data and Evaluating Treatment Effect Heterogeneity in Randomized Clinical Trials · Harvard · PhD
Tried and failed
naive uncalibrated inverse propensity score weighting applied to treatment effect estimation with misspecified imputations. Reason: asymptotic bias persists and does not diminish with sample size under misspecified initial outcome imputations
Causal Inference in Complex Observational Settings with Applications to Electronic Health Record Data · Harvard · PhD
Lost to a baseline
Horvitz-Thompson and Hajec ratio estimators under network interference suffered from significantly larger standard errors/variance compared to the proposed OR and DR estimators due to sensitivity to extreme estimated propensity scores
Essays on Adaptive Methods for Inference and Prediction under Dependence · Duke · PhD
Tried and failed
doubly robust nearest neighbor imputation estimator applied to missing survey data estimation. Outcome: did not generalise. Reason: both the outcome model and the propensity/probability model were misspecified simultaneously
Topics in survey design and nearest neighbor imputation for survey data · Iowa State · PhD
Tried and failed
truncating propensity score weights applied to time-varying causal effect estimation. Outcome: worse than baseline. Reason: truncating weights distorted covariate balance over sequential treatment paths, amplifying bias and root mean squared error
Toward Robust and Transparent Estimation of the Effects of Time-dependent Interventions · Harvard · PhD
Tried and failed
high-dimensional propensity score with tree-based models applied to causal treatment effect estimation. Reason: variance-governing parameters impacting treatment and outcome relationships caused large estimation bias
Machine Learning Methods for Decision Making Inference in Healthcare · Georgia Tech · PhD
Inability to control for unmeasured confounding and structural cohort segregation
Propensity score adjustments failed to remove bias when key confounders such as activity levels or social networks were unobserved. Similarly, adding coarse area level proxies could not resolve unmeasured confounding, and matching broke down when cohorts were structurally segregated by baseline productivity.
Tried and failed
propensity score weighting and test-then-pool synthesis applied to integrating external heterogeneous control datasets. Outcome: did not generalise. Reason: unmeasured confounders and varying study-specific intercepts caused severe type I error inflation and substantial bias
Methods for the Design and Analysis of Clinical Trials: Uncertainty Directed Randomization and Data Synthesis · Harvard · PhD
Tried and failed
adding area-level covariates to propensity score models applied to controlling unmeasured confounding in observational studies. Outcome: no signal. Reason: area-level proxies lacked sufficient granularity to adjust for individual-level unmeasured confounding or shift effect estimates
Leveraging Geographic Information for Causal Inference in Pharmacoepidemiology · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected Propensity Score Matching (PSM) for comparing experimental and control groups because groups were not overly imbalanced across baseline variables and PSM could not address unmeasured confounders
Treatment compliance of male perpetrators of intimate partner violence · Nottingham · Doctorate
Considered and rejected
Considered and rejected: Rejected using propensity score matching due to inability to match on unobserved social network and information campaign variables.
Analyzing the drivers of agricultural technology adoption among smallholder farmers in Uganda · Oxford · PhD
Considered and rejected
Considered and rejected: Rejected propensity score matching because retrospective covariates were limited and omitted-variable bias could not be addressed without an endogenous treatment model.
Unequal starts: the role of different learning environments in the development of inequalities in skills during early childhood · University of Trento · PhD
Considered and rejected
Considered and rejected: Rejected using Propensity Score Matching (PSM) for evaluating co-op vs IOF price impacts because PSM fails when participant groups are structurally segregated by productivity.
THREE ESSAYS ON THE ECONOMICS OF SMALLHOLDER AGRICULTURE IN AFRICA: LABOUR ALLOCATION, ADOPTION OF CONSERVATION AGRICULTURE AND CO-OPERATIVE PARTICIPATION · Saskatchewan · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Evaluate machine learning classification methods against logistic regression for propensity score estimation with imbalanced group sizes in marginal structural models. Blocker: None
An inspection of measurement error inherited in time-varying latent covariate proxies within marginal structural models · UT Austin · PhD
Left open
Log-transform outcome variables and evaluate childless versus mother comparisons via propensity score matching on merged PSID and O*NET data. Blocker: None
Impact of Occupational Flexibility on Labor Market Outcomes of Women Following Childbirth · MIT · Master's
Left open
Apply matching methods or propensity score estimation to the survival model dataset to control for strategic selection in leader tenure and concessions. Blocker: None
The Causes and Consequences of Territorial Nationalism · Harvard · PhD
Left open
Compare standard propensity score weighting with entropy balancing, matching, and stratification methods in added-growth latent growth models via simulation. Blocker: None
Treatment effect estimation using latent growth models and propensity score weights with heterogeneous growth trajectory shapes · UT Austin · PhD
Left open
Implement propensity score matching or multivariate clustering on public Census tract data to select control sites for TOD comparative analysis. Blocker: None
NOT JUST TOD: WHOSE GROWTH? WHOSE NEIGHBORHOOD? – Ethnic-Majority Cohesion, Economic Transformation, and Gentrification Resistance in Hispanic/Latino-Majority Transit-Oriented Neig · Cornell · Master's
Left open
Develop and evaluate methods using large language models for propensity score estimation and causal inference on observational data. Blocker: None
Language Models as Opinion Models: Techniques and Applications · MIT · PhD
Left open
Implement propensity score matching, stratification, machine learning estimators, and trimming methods for latent variable outcome models in lavaan simulation studies. Blocker: None
Latent variable outcome analysis models in a propensity score framework · UT Austin · PhD
Left open
Develop methodology to quantify residual and unmeasured confounding for continuous dose matching and subclassification. Blocker: Lack of concrete statistical approach or mathematical framework specified in the thesis
Observational Data With A Continuous Exposure: Study Design And Outcome Analysis · Penn · PhD
Left open
Apply propensity score matching to balance confounding factors when evaluating hospital nursing resources and surgical patient outcomes. Blocker: Access to restricted hospital discharge databases and nursing survey data
ASSOCIATIONS BETWEEN HOSPITAL NURSING RESOURCES AND THE OUTCOMES OF SURGICAL PATIENTS WITH PROLONGED SURGICAL TIME · Penn · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Murat Unal · Cornell · PhD · 2022
ESSAYS ON DIGITAL EXPERIENCE - Björn Huß · Leibniz Universität Hannover · PhD · 2021
Parenthood and Life Satisfaction: The Consequences of Childbirth, Alternative Pregnancy Outcomes, and Single Parenting on Well-Being in Several Domains of Life - Xun Zhang · Cornell · Master's · 2025
NOT JUST TOD: WHOSE GROWTH? WHOSE NEIGHBORHOOD? – Ethnic-Majority Cohesion, Economic Transformation, and Gentrification Resistance in Hispanic/Latino-Majority Transit-Oriented Neighborhoods - Daniel Xu · Harvard · PhD · 2025
Causal Inference in Complex Observational Settings with Applications to Electronic Health Record Data - Handson Nyakiba Bota · Harvard · Master's · 2025
Comparative Outcomes in Cervical Spine Surgery: A Dual Analysis of Revision Surgeries and Adjacent Segment Degeneration - William Brannon · MIT · PhD · 2025
Language Models as Opinion Models: Techniques and Applications - Marie-Laure Charpignon · MIT · PhD · 2025
Evaluating the Effects of Pharmaceutical Interventions, Social Policies, and Exogeneous Shocks on People's Health and Behavior - Jia-en Jane Hu · MIT · Master's · 2025
Impact of Occupational Flexibility on Labor Market Outcomes of Women Following Childbirth - Emily Greenwood · Georgia Tech · PhD · 2025
Assessing regulatory function of rare and common variants using expression CROP-sequencing - Inga Lisa Pauls · University of Marburg · PhD · 2025
In-Group and Out-Group Perspectives on Content of Social Groups - L Ter-Minassian · Oxford · PhD · 2025
Towards trustworthy AI: from local explanations to causal understanding - A Qadri · Oxford · PhD · 2025
Unveiling dynamics: examining the gendered association between shifting work patterns, labour market changes, and maternity leave on mental health and well-being.
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