[1] CAUSATION: Causal inference (including covariate selection, double robust estimation, double/debiased machine learning), quasi-experimental designs (matching designs, propensity score designs, regression discontinuity designs, interrupted time series designs), causal diagrams, structural causal models, causal mediation analysis, causal decomposition analysis, time-varying treatment regimes.
[2] REPLICATION: Causal replication designs & reproducibility, design replication studies (within-study comparisons) for evaluating quasi-experimental designs.
[3] SURVEY DESIGN: Factorial survey, experimental vignette designs.

Peter M Steiner is a Professor in the Quantitative Methodology: Measurement and Statistics (QMMS) program in the Department of Human Development and Quantitative Methodology at the University of Maryland. Prior to joining the QMMS faculty in fall of 2019, he was a faculty member of the Department of Educational Psychology at the University of Wisconsin-Madison (2010-2019), a research associate at the Institute for Policy Research at Northwestern University (2007-2010), and a researcher and Assistant Professor at the Institute for Advanced Studies in Vienna, Austria (1997-2007). Peter M Steiner received a master’s and doctorate degree in Statistics from the University of Vienna and a master’s degree in Economics from the Vienna University of Economics and Business Administration. His research on causal inference, replication, and factorial surveys has appeared in such journals as Psychological Methods, Multivariate Behavioral Research, Journal of Educational and Behavioral Statistics, Evaluation Review, Sociological Methods & Research, Journal of Causal Inference, or the Journal of the American Statistical Association. In 2019, he received the Causality in Statistics Education Award of the American Statistical Association.

(1) Graphical Models for Causal Inference
(2) Causal Inference & Evaluation
(3) Causal Mediation Analysis