Welcome to QAPS
The Program for Quantitative and Analytical Political Science (QAPS) was established in 2009 to support theoretical and quantitative research in political science and its dissemination. We support graduate students through QAPS fellowships, host post-doctoral research fellows, offer statistical and formal theory consulting, hold quantitative skills workshops, throw conferences, and organize the Quantitative Social Science Colloquium.
Currently, there are no future events. Please contact us if you would like us to provide a methodology workshop that will benefit your research agenda.
We are happy to announce exciting updates from QAPS! We recently welcomed Tolgahan Dilgin as the QAPS Statistical Services Manager. For those who remember Will Lowe, Tolgahan will be serving in a very similar role. Stay tuned for updates on our upcoming workshops, and for information on how to utilize our consultancy services.
Many opioid policies are being implemented at the state level; as one example, 37 states have passed laws limiting the dose and/or duration of opioid prescriptions. However, studying state policy effects can be challenging, especially when states that do and don’t implement the policies differ from one another, and when states implement laws…
A defining feature of the information environment in contemporary China is scripted government propaganda---the government directing newspapers to use specific language when reporting on particular events. Yet due to the mix of syndication and scripting, it is difficult to tell if any given article is explicitly government-directed news. Using…
A hands-on training on Overleaf, LaTeX and BibTeX for students and fellows associated with Center for International Security Studies (CISS). The workshop will include tasks such as using TeX packages for mathematical notation, easily converting hand-written notation to LaTeX code, creating bibliography via integrating Google Scholar, Zotero,…
Estimation of heterogeneous treatment effects is an active area of research in causal inference. Most of the existing methods, however, focus on estimating the conditional average treatment effects of a single, binary treatment given a set of pre-treatment covariates. In this paper, we propose a method to estimate the heterogeneous causal…