Statistics — Page 35
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Machine Learning-Augmented Matching Estimator
The machine learning-augmented matching estimator combines classical nearest-neighbor or propensity-score matching with ML algorithms — such as lasso, random forests, or gradient boosting — to select covariates, estimate propensity scores,...
Machine Learning-Augmented Panel Event Study
The machine learning-augmented panel event study extends the classical panel event study by replacing or augmenting parametric counterfactual models with machine learning estimators — such as LASSO, random forests, or matrix completion — to...
Machine Learning-Augmented Placebo Test
The machine learning-augmented placebo test is a causal-inference validation technique that uses flexible ML estimators — such as causal forests, LASSO, or double/debiased ML — to conduct falsification checks on an identification strategy....
Machine Learning-Augmented Propensity Score Matching
Machine learning-augmented propensity score matching (ML-PSM) replaces the traditional logistic regression used to estimate propensity scores with flexible machine learning algorithms — such as gradient boosted trees, random forests, or LAS...
Machine Learning-Augmented Sensitivity Analysis for Causality
Machine learning-augmented sensitivity analysis combines flexible ML estimators with formal robustness checks to assess how much unmeasured confounding would be required to overturn a causal finding. Rooted in Chernozhukov et al.'s double/d...
Machine Learning-Augmented Synthetic Control Method
The machine learning-augmented synthetic control method extends the classical synthetic control estimator by using penalized regression or other ML algorithms — such as lasso, ridge, or random forests — to construct the donor weights and to...
Machine learning-augmented causal impact analysis
Machine learning-augmented causal impact analysis combines quasi-experimental counterfactual reasoning with flexible ML prediction models to estimate the causal effect of an intervention on a time series outcome. Building on Brodersen et al...
Machine learning-augmented difference-in-differences
Machine learning-augmented DiD combines the classic difference-in-differences identification strategy with flexible ML estimators for nuisance functions — the propensity score and the outcome regression — to obtain valid causal estimates ev...
Machine learning-augmented doubly robust estimation
Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regressi...
Machine learning-augmented event study design
Machine learning-augmented event study design combines the standard event study framework — which traces outcome dynamics around a treatment date — with ML-based methods such as double/debiased machine learning (DML) or regularized regressi...
Machine learning-augmented instrumental variables
Machine learning-augmented instrumental variables combines the causal identification power of classical IV with modern high-dimensional machine learning — using methods such as LASSO, random forests, or neural networks to select valid instr...
Machine learning-augmented propensity score weighting
Machine learning-augmented propensity score weighting (ML-PSW) replaces logistic regression with flexible ML algorithms — such as gradient boosting, LASSO, or random forests — to estimate the propensity score, then uses inverse probability...
Machine learning-augmented regression discontinuity design
Machine learning-augmented regression discontinuity design (ML-RDD) combines the sharp identification logic of classical RDD — exploiting a known assignment cutoff in a running variable — with flexible, data-adaptive ML methods for bandwidt...
Maki Cointegration Test
The Maki cointegration test extends cointegration testing to allow for an unknown number of endogenously-determined structural breaks in the cointegrating relationship. Introduced by Maki (2012), it builds on Gregory and Hansen (1996), enab...
Malmquist Productivity Index
The Malmquist Productivity Index (MPI) is a non-parametric measure of total factor productivity (TFP) change over time. Formally grounded in distance functions by Caves, Christensen, and Diewert (1982) and operationalized using Data Envelop...
Mann-Whitney U test
The Mann-Whitney U test is the nonparametric alternative to the independent samples t-test, comparing two independent groups by ranking all observations together rather than relying on their means. It was introduced by H. B. Mann and D. R....
Many-Facet Rasch Measurement
Many-facet Rasch measurement (MFRM) extends the basic Rasch model to assessments mediated by raters. Beyond examinee ability and item difficulty, it adds explicit parameters for rater severity and for any other facet of the rating situation...
Marginal Structural Model
A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations...
Marginal structural model in education research
A marginal structural model (MSM) is a causal inference technique that uses inverse probability weighting to estimate the effect of a treatment or educational intervention that changes over time. Introduced by Robins, Hernán and Brumback (2...
Market Entry Timing Hazard Analysis
Market entry timing hazard analysis models both whether and when a firm enters a new market or emerging industrial subfield, treating time-to-entry as a survival outcome. Will Mitchell's 1989 study of incumbents facing emerging subfields fr...
Market Microstructure Analysis
Market microstructure analysis studies how prices form from tick-level trade and quote data, examining order-book dynamics, the bid-ask spread, and price discovery. The modern econometric framework was set out by Hasbrouck (2007) and extend...
Markov-Switching Model
The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically...
Matching Estimator
The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory b...
Matching Methods
Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Co...