by Tim Mensinger & Dominik Liebl
You can download the presentation slides by clicking here, or you can view them directly on GitHub here.
The companion repository to the working paper Fair Causal Inference with Functional Data is found at timmens/functional-treatment-effects.
- Chernozhukov (2018). The Econometrics Journal: Double/debiased machine learning for treatment and structural parameters
- Robins, Rotnitzky and Zhao (1994). Journal of the American Statistical Association: Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- Robins and Rotnitzky (1995). Journal of the American Statistical Association: Semiparametric Efficiency in Multivariate Regression Models with Missing Data
- Scharfstein, Rotnitzky and Robins (1999). Journal of the American Statistical Association: Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
- Rosenbaum and Rubin (1983). Biometrika: The central role of the propensity score in observational studies for causal effects
- Jinyong Hahn (1998). Econometrica: On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
- Wager (2020). Stats 361: Causal inference
- Liebl and Reimherr (2022). Book chapter in Contributions to Statistics. Springer: Fast and Fair Simultaneous Confidence Bands for Functional Parameters
- Telshow and Schwartzman (2022). Journal of Statistical Planning and Inference: Simultaneous Confidence Bands for Functional Data Using the Gaussian Kinematic Formula
- Email: tmensinger[at]uni-bonn.de
- Website: tmensinger.com