2022 Fall

The Methodology Working Group workshops are held on Wednesdays from 10:30-11:30 am.

09/21/2022: An Introduction to Neural Networks
Presenters: Bhuv Jain and Dimitrios Tanoglidis
Resources: https://github.com/dtanoglidis/ML_Edu_Demos/blob/main/UPenn_Methodology_DL.ipynb & https://github.com/dtanoglidis/ML_Edu_Demos

10/12/2022: FSRDC Data Types & Becoming a Special Sworn Status Researcher
Presenters: Joe Ballegeer (U.S. Census Bureau) and Jeff Lin (U.S. Census Bureau)

10/19/2022: Nonstable Population Relations and Applications in Aging, Labor, Health, Immigration (R Code)
Presenter: Michael Lachanski

10/26/2022: Kinship Estimation Using the DemoKin Package in R (R Code)
Presenters: Hal Caswell (University of Amsterdam) and Kai Feng
Papers:

• • Caswell, Hal. 2019. The Formal Demography of Kinship: A Matrix Formulation. Demographic Research 41(24): 679-712.
  • Caswell, Hal. 2020. The Formal Demography of Kinship II: Multistate Models, Parity, and Sibship. Demographic Research 42(38): 1097-1144
  • Caswell, Hal. and Xi Song. 2021. The Formal Demography of Kinship III. Kinship Dynamics with Time-Varying Demographic Rates. Demographic Research 45(16):517-546.
  • Caswell, Hal. 2022. The Formal Demography of Kinship IV: Two-Sex Models. bioRxiv.

Data: DemoKin Package on GitHub

11/02/2022: Modern Regression Discontinuity Designs (R Code) (Data)
Presenter: Michael Lachanski

Papers:

• • Cattaneo, M. D., & Titiunik, R. (2022). Regression discontinuity designs. Annual Review of Economics, 14, 821-851.
  • Cattaneo, M. D., Keele, L., & Titiunik, R. (2021). Covariate Adjustment in regression discontinuity designs. arXiv preprint arXiv:2110.08410.
  • Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2019). A practical introduction to regression discontinuity designs: Foundations. Cambridge University Press.

11/09/2022: Double/De-biased Machine Learning for Causal Inference (R Code) 

Presenter: Cheney Yu

Papers:

• • Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. “Double / Debiased Machine Learning for Treatment and Structural Parameters,” Econometrics Journal 21(1):1-68.
  • Felderer, B., Kueck, J., & Spindler, M. (2022). Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-Mode Online Panel. Social Science Computer Review, 08944393221095194.
  • Robinson, Peter. 1988. “Root-N-Consistent Semiparametric Regression,” Econometrica 56(4):931-954.

11/16/2022: Marginal Structural Models (R Code) (Data)
Presenter: Sukie Yang

11/30/2022: Word Embedding Using Occupation Data
Presenter: Wenhao Jiang (NYU)

12/07/2022: Dyadic Sequence Analysis
Presenter: Allison Dunatchik

2022 Summer

08/30/2022: Computer Vision in the Social Sciences
Presenter: Doron Shiffer-Sebba
Papers:

• • Torres, Michelle and Francisco Cantú. 2021. “Learning to See: Convolutional Neural Networks for the Analysis of Social Science Data.” Political Analysis 30(1): 113-131.
  • Goldstein, Yoav, Nicolas M. Legewie, and Doron Shiffer-Sebba. 2022. “3D Social Research: Analysis of Social Interaction Using Computer Vision.” Sociological Methods and Research, Conditionally Accepted.

06/21/2022: Estimation Philosophy
Presenters: Michael Lachanski & Richard Patti
Paper: Lundberg, Ian, Rebecca Johnson, and Brandon M. Stewart. 2021.”What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory.” American Sociological Review 86(3):532-65. 

06/10/2022: GitHub for Research Reproducibility and Transparency
Presenter: Hunter York

06/07/2022: Spatial Bayesian Regression (R Code) (Data)
Presenters: Treva Tam & Eugenio Paglino
Paper: Morris, Mitzi, Katherine Wheeler-Martin, Dan Simpson, Stephen J. Mooney, Andrew Gelman, and Charles DiMaggio. 2019. “Bayesian Hierarchical Spatial Models: Implementing the Besag York Mollié Model in Stan.” Spatial and Spatio-temporal Epidemiology 31:100301. [Alternative Access via Research Gate]
Book: Blangiardo, Marta, and Michela Cameletti. 2015. Spatial and Spatio-Temporal Bayesian Models with R-INLA. Chichester, England: Wiley. [Penn Libraries Online Access]

05/24/2022: Difference-in-Differences (Introduction) (R Code)
Presenter: Alexander Adames
Paper:  Angrist, Joshua D. and Jörn-Steffen Pischke. 2009. “Chapter 5: Parallel Worlds: Fixed Effects, Difference-in Differences, and Panel Data Angrist.” Pp. 221-246 in Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton. NJ: Princeton University Press. [Penn Libraries Online Access]

2020

05/29/2020: Data Visualization Mini-Hackathon