R for GIS

Presenter: Oscar Pocasangre

R for GIS

The objectives for the workshop are:

1. Learn how to map point into a map
2. Merge data with spatial data
3. Make a choropleth map

Link to Workshop Materials

• Data files

Causal Inference in Observational Studies

Presenter: Tom Leavitt

Causal Inference in Observational Studies: Assignment Mechanism Models and the Role of Covariate Balance

Observational studies posit that units’ treatment assignment probabilities are governed by an unknown function of baseline covariates such that, if units have identical baseline covariates, then they must have identical treatment assignment probabilities. But even if all potentially confounding covariates are observed, exact balance on all of them is both mathematically and practically infeasible; hence, researchers often conduct tests of covariate balance to assess whether a given design has “enough” balance such that the study can be analyzed as if it is a block, uniform randomized experiment. In this presentation, I explain that the null hypothesis of such tests is not covariate balance, per se, but the proposition that treatment assignment probabilities are uniformly distributed within covariate blocks. I then formally demonstrate that tests of this null hypothesis are biased against a subset of alternative hypotheses that probabilities are nonuniformly distributed within blocks, and subsequently develop a power analysis that enables researchers to discern the specific alternative hypotheses for which the aforementioned null hypothesis is biased and unbiased, respectively. The argument and method advanced in this presentation have the potential to shed light on debates about the role that models of the assignment mechanism and subsequent assessments of covariate balance play in drawing causal inferences from observational data.

Formal Models and Lab Experiments

Presenter: Giovanna Invernizzi

CELSS Opportunities

• CELSS offers trainings on Z-tree (2-5pm on December 1st) and O-tree (2-4pm on December 8th)

• Link to sign up is here

Link to Workshop Materials

• Slides for the presentation

Endogenous Units of Analysis

Presenter: Tara Slough

Plan

Empirical political science research increasingly emphasizes experimental and observational methods for causal inference. However, a surprising number research designs seek to make causal inferences on the basis of “endogenous units of analysis.” Units are endogenous when the identity, survival, or composition of the units upon which outcomes are being measured is plausibly endogenous to the treatment being studied. I show that when units are endogenous to treatment, standard causal estimands are undefined. This finding holds for both individual- and cluster-assigned treatments. Using principal stratification, I describe the Survivor Average Causal Effect (SACE) as a relevant estimand for a broad set of cases in which units of analysis may be endogenous to treatment. However, our ability to bound informative estimates of the SACE depends critically on the research design and the process through which treatment affects the composition of units. In the presentation, I will discuss the implications of these findings for works seeking to make causal inferences using long-run historical data and works drawing upon downstream analysis of experiments.

Conjoint Experiments in Practice

Presenter: Erin York and Alicia Cooperman

Plan

This workshop will address the motivation, design, and analysis of conjoint experiments and a workshop practice example

Link to Workshop Materials

• Slides for the presentation