This article analyzes academic freedom worldwide with newly available cross-national data. The literature principally addresses impingements on academic freedom arising from religion or repressive states. Academic freedom has broadly increased since 1945, but we see episodic reversals, including in recent years. Conventional work emphasizes the uniformity of international institutional structures and their influence on countries. We attend to the heterogeneity of international structures in world society and theorize how they contribute to ebbs and flows of academic freedom. Post-1945 liberal international institutions enshrined key rights and norms that bolstered academic freedom worldwide. Alongside them, however, illiberal alternatives coexisted. Cold War communism, for instance, anchored cultural frames that justified greater constraints on academia. We evaluate domestic and global arguments using regression models with country fixed effects for 155 countries from 1960 to 2022. Findings support conventional views: academic freedom is associated positively with democracy and negatively with state religiosity and militarism. We also find support for our argument regarding heterogeneous institutional structures in world society. Country linkages to liberal international institutions are positively associated with academic freedom. Illiberal international structures and organizations have the opposite effect. Heterogeneous institutions in world society, we contend, shape large-scale trajectories of academic freedom.

Researchers are often interested in treatment effects on outcomes that are only defined conditional on posttreatment events. For example, in a study of the effect of different cancer treatments on quality of life at end of follow-up, the quality of life of individuals who die during the study is undefined. In these settings, naive contrasts of outcomes conditional on posttreatment events are not average causal effects, even in randomized experiments. Therefore, the effect in the principal stratum of those who would have the same value of the posttreatment variable regardless of treatment (such as the survivor average causal effect) is often advocated for causal inference. While principal stratum effects are average causal effects, they refer to a subset of the population that cannot be observed and may not exist. Therefore, it is not clear how these effects inform decisions or policies. Here we propose the conditional separable effects, quantifying causal effects of modified versions of the study treatment in an observable subset of the population. These effects, which may quantify direct effects of the study treatment, require transparent reasoning about candidate modified treatments and their mechanisms. We provide identifying conditions and various estimators of these effects along with an applied example.