Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, March 3, where Arthur Yu (University of Chicago) presents research on "Beyond LATE: Identification of ATEs of Always-Takers and Never-Takers." *Abstract*: In the presence of heterogeneous treatment effects, instrumental variable (IV) estimation point identifies the local average treatment effect, an average treatment effect (ATE) for compliers. This paper provides a set of identification results that extrapolate the LATE to the ATEs of always-takers and never-takers. We first show that the ATEs of always-takers and never-takers can be written as the weighted average of marginal treatment effect (MTE) functions. We then demonstrate that, under additional parametric assumptions on these MTE functions, we can point identify the ATEs of always-takers and never-takers. In the absence of these parametric assumptions we can construct bounds for the ATEs of always-takers and never-takers by linear programming developed in Mogstad et al. (2018), which performs better than the competing partial identification strategies. We illustrate the proposed methodology using a simulation study and an application based on Kern and Hainmueller (2009). We find that exposure to West German television reduces support for communism among never-takers. These never-takers, who would not watch West German TV even if they had improved access, act as-if they anticipate the effect of watching West German TV and thus opt out of exposure. *Zoom link*: https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all on Wednesday! Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/