• Start date: 10 October 2022
• End date: In progress

Code Walkthrough of UPenn Medical's Propensity Score Modelling course. I aim to complete the course assignments in R, then challenge myself further by redoing them with Python, and go further by applying these techniques to a new dataset outside the course. Code will be available for both.

From Jason Bryer's Introduction to Propensity Score Methods with R:

The use of propensity score methods (Rosenbaum & Rubin, 1983) for estimating causal effects in observational studies or certain kinds of quasi-experiments has been increasing in the social sciences (Thoemmes & Kim, 2011) and in medical research (Austin, 2008) in the last decade.

Propensity score analysis (PSA) attempts to adjust selection bias that occurs due to the lack of randomization.

Analysis is typically conducted in two phases where in phase I, the probability of placement in the treatment is estimated to identify matched pairs or clusters so that in phase II, comparisons on the dependent variable can be made between matched pairs or within clusters. R (R Core Team, 2012) is ideal for conducting PSA given its wide availability of the most current statistical methods vis-à-vis add-on packages as well as its superior graphics capabilities.

• Lecture 1.1 - Notations, Potential Outcomes, Counterfactuals
• Lecture 1.2 - Hypothetical Interventions, FPCI, Subpopulations
• Lecture 1.3 - Assumptions, Stratification Calculation
• Lecture 1.4 - Active Comparator and Incident User Design
• Lecture 2.1 - Confounds, DAGs, D-separation, Joint probability Distribution
• Lecture 2.2 - Confounding Revisited, Disjunctive Cause Criterion
• Lecture 3.1 - Matching Techniques, Mahalanobis distance

• Quiz 1.1
• Quiz 1.2
• Quiz 1.3 -- graded
• Quiz 2.1
• Quiz 2.2 -- graded
• Quiz 3.1

tbc