• Will be available Monday.

• Due by end of day May 19th.

• You may use your book and course materials.

• There are two parts:
  1. Part one multiple choice questions and short answer questions.
  2. Part two has a small data set to analyze with R, then answer some interpretation questions.

• Put your answers in the Rmarkdown file and submit the PDF file. Please do not post your answers online!

• Fernando Figueres (7.23) http://rpubs.com/ffigueres/d606-hwpres
• Mohamed Zaman (7.39) http://rpubs.com/thasleem/d606_presentation
• Michael O’Donnell (8.3)
• Habib Hkan (8.7) https://www.youtube.com/watch?v=Y3pgXd7lwkI&feature=youtu.be https://api.rpubs.com/habibkhan89/494285
• Weijian Cai (7.35) https://youtu.be/wuxMA2jU6n8 http://rpubs.com/weicai/494213

My research interest is in propensity score methods. Propensity score analysis (PSA) is a quasi-experimental design used to estimate causality from observational studies. It is generally conducted in two phases:

1. Estimate propensity scores (i.e. probability of being in the treatment) using the observed covariates.
   1. Check balance
   2. Re-estimate propensity scores
2. Estimate effect sizes using typical group differences (e.g. t-tests)

Areas I have worked on:

• Multilevel PSA (see multilevelPSA R package)
• Matching with non-binary treatments (see TriMatch R package)
• Bootstrapping PSA (see PSAboot R package)

This has been a great semester. Please don’t hesitate to reach out:

• Email: jason.bryer@gmail.com
• Github: http://github.com/jbryer
• Personal Website: http://bryer.org
• LinkedIn
• Twitter: jbryer

Over the summer, the course website will be moved to spring2019.data606.net.

You can download all course materials on Github. Click the clone or download link to download a zip file.