The Data-Generating Process and Scientific Inference

Richard J. Butler, Matthew J. Butler and Barbara L. Wilson

Chapter 4 in Advanced Statistics for Health Research, 2023, pp 65-82 from World Scientific Publishing Co. Pte. Ltd.

Abstract: The Data-Generating Process (DGP) is a description of how an outcome, Y, is determined by independent variables, X, for all the population under consideration. In prior chapters, we saw how sample means, variances, covariances of a subset of those Y s and Xs were merged to generate OLS orthogonal projections (which are the predicted values of Y in the X-plane) to estimate key features of the underlying population DGP. But the population DGP itself is not directly observable. We use sample statistics to make inferences about the population…

Keywords: Nursing; Physician; Healthcare; Public Health; Regression; Orthogonal Projection; Geometric View of Causal Inference; 2SLS; Two-Stage Least Squares; Instrumental Variables; Probits; Logits; Proportional Hazards; Cox Regressions; Quantile Regression; Random Forest Regression; Randomization; Matching; Propensity-Score; Differences-in-Differences; Regression Discontinuity; Fixed Effects; R; SAS; STATA; Research Examples; Empirical Rule; Applied Statistics; Confidence Intervals; VIF; Standard Beta; Histograms; Scatterplots; Regression Gini Index; COVID-19; Gender Wage Differentials; LASSO; Area Under The Curve; AUC; ROC; Decision Trees; Maximum Likelihood; GMM; Data Generating Process; Split Sample Instrumental Variables; Local Average Treatment Effects (LATE); Data Visualization; Omitted Variable Bias; Simultaneous Equations; Measurement Error; Supervised Machine Learning; Panel Data; RGI; Health Professionals (search for similar items in EconPapers)
JEL-codes: C1 I11 I15 (search for similar items in EconPapers)
Date: 2023
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