Statistics — Page 3
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Heterogeneous Treatment Effects
Heterogeneous Treatment Effects is a machine-learning framework that estimates how a treatment effect varies across individuals — the conditional average treatment effect (CATE). It bundles meta-learner strategies such as the T-Learner, S-L...
Heterogeneous treatment effect Causal impact analysis
Heterogeneous treatment effect causal impact analysis extends the Bayesian structural time-series causal impact framework to estimate not just the average effect of an intervention but how that effect varies across subgroups or individual u...
Heterogeneous treatment effect Counterfactual impact evaluation
Heterogeneous Treatment Effect Counterfactual Impact Evaluation (HTE-CIE) extends standard counterfactual impact evaluation by estimating how the causal effect of a policy or intervention varies across subgroups defined by pre-treatment cha...
Heterogeneous treatment effect Doubly robust estimation
Doubly robust estimation of heterogeneous treatment effects (HTE) estimates how the causal effect of a treatment varies across subgroups or individual covariate values. By combining an outcome model and a propensity score model, it retains...
Heterogeneous treatment effect Instrumental variables
Heterogeneous treatment effect IV applies instrumental variables estimation while explicitly acknowledging and modelling that the treatment effect differs across units. Rather than recovering a single average effect, it focuses on the Local...
Heterogeneous treatment effect Panel event study
A heterogeneous treatment effect panel event study estimates how treatment impacts vary across units and over time in a panel setting, allowing each cohort of treated units to have its own dynamic response. Seminal contributions by Sun and...
Heterogeneous treatment effect Placebo test
A placebo test for heterogeneous treatment effects is a falsification strategy used to validate whether estimated variation in treatment effects across subgroups or covariate values is genuine rather than an artifact of model specification,...
Instrumental Variables in Education Research
Instrumental variables (IV) estimation is a quasi-experimental strategy for isolating the causal effect of schooling or educational interventions when assignment to treatment is confounded by unobserved factors. Pioneered in education econo...
Interrupted Time Series
Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagne...
Interrupted Time Series in Education Research
Interrupted time series (ITS) analysis is a quasi-experimental design that estimates the causal effect of an education policy or intervention by examining whether an outcome trend changes abruptly at the point of implementation. Applied to...
Inverse Probability Weighting
Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for...
Inverse Probability Weighting in Education Research
Inverse Probability Weighting (IPW) is a causal inference technique that reweights observational education data to mimic a randomised experiment. Each student or school is assigned a weight equal to the inverse of the probability they recei...
Local Average Treatment Effect
The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose t...
Machine Learning-Augmented Coarsened Exact Matching
Machine Learning-Augmented Coarsened Exact Matching extends Coarsened Exact Matching (Iacus, King & Porro, 2012) by using supervised machine learning to automate and optimise the coarsening step — the discretisation of continuous covariates...
Machine Learning-Augmented Counterfactual Impact Evaluation
Machine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather than imposing parametric functional forms for confounders, ML l...
Machine Learning-Augmented Entropy Balancing
Machine learning-augmented entropy balancing (ML-EB) combines Hainmueller's entropy balancing reweighting scheme with a machine-learning outcome model to produce a doubly-robust causal estimator. By jointly optimising covariate balance weig...
Machine Learning-Augmented Fuzzy Regression Discontinuity
ML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. Where standard fuzzy RDD uses IV-style estimation at a threshold...
Machine Learning-Augmented Interrupted Time Series
Machine Learning-Augmented Interrupted Time Series (ML-ITS) estimates the causal effect of a discrete intervention by training a machine learning model on pre-intervention time series data, projecting a counterfactual trajectory into the po...
Machine Learning-Augmented Inverse Probability Weighting
Machine learning-augmented inverse probability weighting replaces parametric logistic regression with flexible ML algorithms to estimate treatment propensity scores, then reweights the sample to balance treated and control units. By leverag...
Machine Learning-Augmented Marginal Structural Model
The machine learning-augmented marginal structural model combines the causal rigour of Robins et al.'s MSM framework with flexible, data-adaptive ML algorithms for estimating propensity scores and outcome models. By replacing parametric nui...
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...