140.665.01 EXPERIMENTAL AND NON-EXPERIMENTAL DESIGNS FOR ESTIMATING CAUSAL EFFECTS
Presents principles, methods, and applications in drawing cause-effect inferences with a focus on the health sciences. Building on the basis of 140.664, emphasizes statistical theory and design and addresses complications and extensions, aiming at cultivating students’ research skills in this area. Includes: detailed role of design for causal inference; role of models and likelihood perspective for ignorable treatment assignment; estimation of noncollapsible causal effects; statistical theory of propensity scores; use of propensity scores for estimating effect modification and for comparing multiple treatments while addressing regression to the mean; theory and methods of evaluating longitudinal treatments, including the role of sequentially ignorable designs and propensity scores; likelihood theory for instrumental variables and principal stratification designs and methods to deal with treatment noncompliance, direct and indirect effects, and censoring by death.
Upon successful completion of this course, students will 1) Understand causal problems as potential interventions, through the framework of potential outcomes and assignment mechanisms, 2) Understand the role of designs and of different modes of statistical inference, 3) Understand and be able to implement efficient (likelihood) methods with ignorable assignment of treatments, 4) Understand the role of outcome models and of propensity score models, 5) Understand when and how comparisons of longitudinal treatments can be designed as having sequentially ignorable assignment, and learn ways to estimate their causal effects, and 6) Master efficient methods for estimating effects in studies with noncompliance to treatment, direct and indirect effects, and censoring by death.
- Tuesday 3:30 - 4:50
- Thursday 3:30 - 4:50
140.654 or equivalent for matrix representation of multiple linear and logistic regression