This paper studies identification and estimation of average causal effects, such as average marginal or treatment effects, in fixed effects logit models with short panels. Relating the identified set of these effects to an extremal moment problem, we first show how to obtain sharp bounds on such effects simply, without any optimization. We also consider even simpler outer bounds, which, contrary to the sharp bounds, do not require any first-step nonparametric estimators. We build confidence intervals based on these two approaches and show their asymptotic validity. Monte Carlo simulations suggest that both approaches work well in practice, the second being typically competitive in terms of interval length. Finally, we show that our method is also useful to measure treatment effect heterogeneity.

翻译：本文研究了短面板固定效应Logit模型中平均因果效应（如平均边际效应或处理效应）的识别与估计问题。通过将此类效应的可识别集与极值矩问题相关联，我们首先展示了如何无需任何优化计算即可简便地获得这些效应的尖锐边界。我们还考虑了更为简化的外边界方法——与尖锐边界不同，该方法无需任何第一步非参数估计量。基于这两种方法我们构建了置信区间，并证明了其渐近有效性。蒙特卡洛模拟表明两种方法在实际应用中均表现良好，其中第二种方法在区间长度方面通常具有竞争力。最后，我们证明该方法同样适用于处理效应异质性的度量。