AI 中文总结
针对观测面板数据，该研究将偏差感知极小极大方法适配为因果推断方法，经模拟和真实数据验证，其在因子秩拟合不足时仍能保持良好名义覆盖率，且反事实误差较大时估计效应仍显著。
AI 中文摘要
我们开发了一种针对观测型面板数据场景的偏差鲁棒因果推断方法。这类方法通常会对未接受处理的结果进行插补，因此反事实误差会直接传递到估计的处理效应中，而传统标准误会忽略这一误差。我们将为因子模型面板中回归系数估计而开发的偏差感知极小极大方法，适配到因果目标——即受处理组的平均处理效应（该效应需进行插补，且可能在不同个体和时期间存在差异）。该估计量通过加权未处理残差校正插补的反事实，并报告明确考虑剩余误差的区间。在模拟实验中，所提方法在名义覆盖率上表现良好，而广义合成控制等替代方法几乎无名义覆盖率，尤其在因子秩拟合不足时，虽区间更宽但仍保持良好性能。将所开发方法应用于真实数据时，即使反事实误差接近该设计安慰剂通常表现的两倍大小，估计的效应仍保持显著。
英文摘要
We develop a bias-robust causal inference method for observational panel data settings. Such methods typically impute untreated outcomes, so counterfactual error passes straight into the estimated treatment effect while conventional standard errors ignore it. We adapt bias-aware minimax methods, developed for estimating regression coefficients in factor-model panels, to a causal target: the average effect on the treated, which has to be imputed and may vary across units and periods. The estimator corrects the imputed counterfactual with weighted untreated residuals and reports intervals with an explicit allowance for the error that remains. In simulations the proposed method holds nominal coverage where alternatives such as the generalized synthetic control have almost none, especially when the factor rank is underfitted, at the cost of wider intervals. By applying the developed methodology to real data the estimated effect remains significant for counterfactual errors nearly twice the size that the design's placebos typically exhibit.
Comments12 pages, 2 figures, 5 tables
