 We furnish an algorithm to estimate the causal parameter , which utilizes principal component regression (PCR) as a key subroutine. We argue that PCR implicitly de-noises the observations, which are corrupted by idiosyncratic measurement error, and thus advocate for its usage in panel data settings. Formally, we establish consistency and asymptotic normality of the estimated causal parameter.
<clarity>  We furnish an algorithm to estimate the causal parameter , which utilizes principal component regression (PCR) as a key subroutine. We argue that PCR implicitly de-noises the observations, which are corrupted by idiosyncratic measurement error, and thus advocate for its usage in panel data settings. Formally, we establish consistency and asymptotic normality of the estimated causal parameter.
 We furnish an estimator for this causal parameter and establish its consistency and asymptotic normality of the estimated causal parameter.