When Do Treatment Changes Identify Causal Effects?
处理变化何时能够识别因果效应？AI 解读 ↓
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处理变化何时能够识别因果效应？
- 研究问题
- 以处理变化而非处理水平识别因果效应，需要哪些结构限制，与控制历史变量或差分结果的策略何时等价、何时互不包含？
- 方法与思路
- 作者刻画两类假设互不嵌套的结构模型，分析随机游走下的等价关系及替代工具变量模型，并研究部分线性双向固定效应回归。
- 材料陈述的贡献
- 摘要称，等价关系支持过度识别检验；在特定条件下，同时差分结果与处理的回归具有由两类识别假设带来的双重稳健含义。
- 阅读关注点
- 应核对两类结构模型的全部限制、随机游走与无动态效应分别承担的作用，以及双向固定效应一致性和过度识别检验的条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出完整识别假设和检验形式，差分处理本身不能保证消除混杂。
原文摘要
This paper clarifies the identifying assumptions underlying causal inference based on treatment changes rather than levels, and their relationship to conventional identification strategies. We characterize two structural models, with non-nested assumptions, under which treatment-change identification is valid conditional on observed covariates by differencing out time-constant confounders that are additive in the treatment equation. The assumptions underlying treatment changes are generally not nested with those of methods relying on treatment levels, such as selection-on-observables strategies that control for past outcomes, treatments, and covariates, or difference-in-differences approaches that difference outcomes rather than treatments over time. We show, however, that under a random-walk restriction on the treatment process, exploiting treatment changes for identification is equivalent to using treatment levels given lagged treatment. This and other equivalence results motivate overidentification tests based on methods considering treatment levels and changes. Under an alternative model that does not assume a random walk but instead rules out dynamic treatment effects (among other conditions), treatment changes can still be used as an instrument to identify a treatment effect that is constant given covariates. However, without random walk, different identification strategies are generally not nested. In partially linear models, the non-nesting results carry a double robustness implication for two-way fixed effects regression that differences both the outcome and the treatment over time, which under certain conditions remains consistent if either the treatment-change assumption or the parallel-trends assumption holds. We characterize the causal models consistent with each method, run simulations for illustration, and present an empirical application to cigarette demand.