Compute DiD and validate parallel trends

You are given observational/experiment-like panel data as three equal-length arrays:

• period[i] : an integer time period label for observation i (e.g., -2, -1 are pre; 0, 1 are post).
• group[i] : 1 if the unit is in the treatment group, 0 if in control.
• outcome[i] : numeric outcome.

Tasks:

1. Difference-in-differences (DiD) : compute the DiD estimate using:
   • pre period = the latest pre period ( max{period < 0} )
   • post period = the earliest post period ( min{period >= 0} )
   Define:
   • $\bar{Y}_{T,post}$ , $\bar{Y}_{T,pre}$ , $\bar{Y}_{C,post}$ , $\bar{Y}_{C,pre}$ as the mean outcomes for Treatment/Control at the chosen post/pre periods.
   • $\text{DiD} = (\bar{Y}_{T,post}-\bar{Y}_{T,pre}) - (\bar{Y}_{C,post}-\bar{Y}_{C,pre})$ .
2. Parallel trends / pre-trend validation : given a numeric threshold , validate that treatment and control follow similar trends in the pre period by checking:
   • For each pre period $t$ , compute $d_t = \bar{Y}_{T,t} - \bar{Y}_{C,t}$ .
   • The pre-trend is considered valid if max(d_t) - min(d_t) <= threshold across all pre periods.

Output:

• Return the DiD estimate (float) and a boolean indicating whether the pre-trend validation passes.

Assumptions:

• There is at least one pre period and one post period.
• Means are computed over all rows matching the period/group filters.