AI 中文总结
研究因果推断中溢出和干扰带来的挑战，比较基于控制和基于预测的两类反事实方法识别因果效应的条件，通过模拟和实证说明方法优缺点，指出在特定溢出效应下基于预测的方法能更可靠识别因果参数。
AI 中文摘要
溢出和干扰给因果推断带来了根本挑战，因为分配给一个单元的处理可能会影响其他单元的结果，这违反了大多数实证策略所依据的无干扰假设。现有方法通常依赖关于交互结构的强假设或需要存在未受污染的控制单元来估计相关因果参数。我们在潜在结果框架内重新审视这一识别挑战，比较使用两类反事实方法（基于控制的反事实方法和基于预测的反事实方法）识别因果效应的条件。通过模拟和实证应用，说明了每种方法的主要优缺点。结果表明，在存在普遍或定义不明确的溢出效应时，基于控制的反事实方法要么无法使用，要么存在严重识别问题，而基于预测的反事实方法至少在短期内可以更可靠地识别一些感兴趣的因果参数。
英文摘要
Spillovers and interference pose fundamental challenges for causal inference, as treatment assigned to one unit may affect the outcome of others, violating the no-interference assumption underlying most empirical strategies. Existing approaches, based on partial interference, exposure mapping, spatial, network, or structural frameworks, typically rely on strong assumptions about interaction structures or require the existence of uncontaminated control units to estimate relevant causal parameters. We revisit this identification challenge within the potential outcomes framework and compare the conditions under which causal effects can be identified using two broad classes of counterfactual methods: control-based counterfactual methods (CBCMs), such as matching and difference-in-differences designs, and forecast-based counterfactual methods (FBCMs), including interrupted time-series and machine learning control methods. We show under which circumstances CBCMs and FBCMs identify average direct and spillover effects. Through simulations and an empirical application, we illustrate the main advantages and limitations of each approach. We show that, in the presence of pervasive or ill-defined spillover effects, CBCMs either cannot be used or entail severe identification concerns, whereas FBCMs can more credibly identify some of the causal parameters of interest, at least in the short term.
