How control for a pre-treatment outcome $Y_0$ if is a strong confounder while avoiding regression to the mean bias for treatment effect on $Y_1$? I'm facing a dilemma in a pre/post cohort matching analysis for a healthcare intervention:

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*Matching on the pre-treatment outcome $Y_0$ (a continuous variable) will likely lead to regression to the mean bias in the treatment effect estimate on $Y_1$.

*However, $Y_0$ is a strong confounder that's causally associated with the treatment $A$ and $Y_1$, therefore we want to control for $Y_0$ in some capacity.

Given ANCOVA is the standard remedy for removing regression to the mean effects, is it valid to not match (exact or propensity score) on $Y_0$ but include $Y_0$ in the post-matching outcome model? This protects us from both regression to the mean bias and confounder bias.

I assume that the baseline (pre-treatment) was recorded before, then intervention was applied on a subset of the population.The expected difference in means at baseline is zero. The correct linear model should be:
post = pre + treatment
If treatment is applied before the baseline was recorded, the change score should be used (post-pre) as response variable. Here you don't expect zero difference at baseline
The best practice is related to the design.
Jeffrey Walker provide a clear and extensive explanation of the problem and
reports each case with examples in R (https://www.middleprofessor.com/files/applied-biostatistics_bookdown/_book/issues.html#issues-pre-post)
