Statistics — Page 7
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Regression Discontinuity in Policy Evaluation
Regression discontinuity (RD) is a quasi-experimental design for estimating the causal effect of a policy that is assigned by a sharp threshold on some continuous eligibility score — an income line for a benefit, a test score for a scholars...
Regression Kink Design
The Regression Kink Design is a quasi-experimental method that estimates a causal effect when a policy rule creates a change in slope (a kink) — rather than a jump — at a known threshold of a running variable. It was formalised as a general...
Regression discontinuity design in education research
Regression discontinuity design (RDD) in education research exploits a score-based eligibility cutoff — such as a test score threshold, GPA requirement, or age cutoff — to estimate the causal effect of a program, intervention, or policy on...
Remittance Impact Evaluation
Remittance impact evaluation is the set of causal-inference designs used to estimate what remittances actually do to the households that receive them — their effect on investment, schooling, child labor, entrepreneurship, and labor supply —...
Reproduction Number
The basic reproduction number R0 is the expected number of secondary infections produced by a single infectious individual introduced into a fully susceptible population. Formally defined and computationally grounded by Diekmann, Heesterbee...
Robust Causal Impact Analysis
Robust Causal Impact Analysis extends the Bayesian structural time-series CausalImpact framework (Brodersen et al., 2015) by embedding systematic robustness checks — in-time placebo tests, in-space placebo controls, covariate sensitivity an...
Robust Counterfactual Impact Evaluation
Robust Counterfactual Impact Evaluation (Robust CIE) strengthens causal impact estimates by combining multiple quasi-experimental estimators, placebo tests, and formal sensitivity analyses. Rather than relying on a single method, it cross-v...
Robust Difference-in-Differences
Robust Difference-in-Differences is a family of modern DiD estimators designed to remain valid when treatment timing is staggered across units and treatment effects are heterogeneous over time or across groups. Classical two-way fixed-effec...
Robust Fuzzy Regression Discontinuity
Robust Fuzzy Regression Discontinuity Design estimates a local average treatment effect (LATE) at a threshold where crossing the cutoff raises — but does not guarantee — treatment receipt. Introduced by Calonico, Cattaneo, and Titiunik (201...
Robust Instrumental Variables
Robust Instrumental Variables estimation extends standard IV and two-stage least squares (2SLS) by guarding against weak-instrument bias and non-standard inference. Methods such as the Anderson-Rubin test, Limited Information Maximum Likeli...
Robust Interrupted Time Series
Robust Interrupted Time Series Analysis is a quasi-experimental method that estimates the causal effect of a policy or intervention on an aggregate outcome over time, using segmented regression fitted with outlier-resistant or heteroskedast...
Robust Inverse Probability Weighting
Robust Inverse Probability Weighting is a causal inference estimator that reweights observed units by stabilized or trimmed propensity score weights, then applies sandwich or bootstrap variance estimation to guard against model misspecifica...
Robust Marginal Structural Model
Robust Marginal Structural Models (robust MSMs) extend the standard MSM framework — which uses inverse probability of treatment weighting to handle time-varying confounding — by pairing IPTW estimation with sandwich (robust) standard errors...
Robust Matching Estimator
The robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alik...
Robust Panel Event Study
A robust panel event study extends the standard panel event study design by applying heteroskedasticity- and autocorrelation-robust (HAC) standard errors and, where staggered treatment adoption exists, interaction-weighted estimators that r...
Robust Propensity Score Matching
Robust Propensity Score Matching (robust PSM) is a quasi-experimental causal inference method that pairs treated and control units on their estimated probability of receiving treatment (the propensity score), then estimates the average trea...
Robust Propensity Score Weighting
Robust Propensity Score Weighting extends standard inverse probability weighting by incorporating safeguards against misspecification of the propensity score model and extreme weights. It combines techniques such as weight trimming, overlap...
Robust Synthetic Control Method
The robust synthetic control method extends the classic synthetic control estimator by providing statistically valid uncertainty quantification and inference. Developed by Cattaneo, Feng and Titiunik (2021), it addresses a core limitation o...
SEIR Model
The SEIR model is a deterministic compartmental model that partitions a closed population into four epidemiological states: Susceptible (S), Exposed (E), Infectious (I), and Recovered (R). It extends the classic SIR framework by explicitly...
SIR Model
The SIR model is a foundational mathematical framework for describing the spread of infectious diseases through a population. Introduced by William Ogilvy Kermack and Anderson Gray McKendrick in 1927, it partitions a closed population of si...
Segmented Assimilation Test
The segmented assimilation test, formalized by Alejandro Portes and Min Zhou in 1993, examines why the children of immigrants follow strikingly different trajectories rather than converging on a single mainstream path. Against the classic a...
Sensitivity Analysis for Causality
Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estima...
Sensitivity Analysis for Unmeasured Confounding
Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Ro...
Sensitivity analysis for causality in education research
Sensitivity analysis for causality in education research tests how robust a quasi-experimental finding is to unmeasured confounding. Rather than assuming all bias has been removed, it quantifies how large a hidden bias would need to be to o...