Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies

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A Potential Outcomes View of Value-Added Assessment in Education (2004, methodology): “In this discussion we approach value-added assessment from a “potential outcomes” (Rubin Causal Model, RCM) point of view ( Rubin 1974, 1978, 2003; Holland 1986; Little and Rubin 2001), with the goal of clarifying the estimation goals and understanding the limitations of data for the types of compa”
A paradox from randomization-based causal inference (2014, methodology): “They are now introduced at the beginning of many causal inference courses and textbooks (e.g., Rubin, 2004; Imbens and Rubin, 2015).”
What Would It Take to Change an Inference? Using Rubin’s Causal Model to Interpret the Robustness of Causal Inferences (2013, background): “Rubin (2004) would differentiate possible outcomes using Y(1) for the treatment and Y(0) for the control, but the parenthetical expressions become awkward when part of a larger function.”
Using the Propensity Score Method to Estimate Causal Effects (2013, background): “In Rubin’s causal model (see Rubin, 2004, for a summary), Y1i and Y0i are potential earnings for individual i when i receives (Y1i) or does not receive training (Y0iÞ.”