Thank you for sharing this and those links. It would be useful to build a quantitative and qualitative summary of how and when early interventions in childhood lead to long-term gains. You can have a positive effect later in life and still have decay (or growth, or constant, or a mix). In our case, we are particularly interested in terms of subjective wellbeing rather than income alone.
One (small) reason one might start with a larger prior on the constant effects model is to favor simplicity
I am a bit rusty on Bayesian model comparison, but—translating from my frequentist knowledge—I think the question isn’t so much whether the model is simpler or not, but how much error adding a parameter reduce? Decay probably seems to fit the data better.
Any model with more degrees of freedom will always fit the data (that you have!) better. A decay model nests a constant effects model, because the decay parameter can be zero.
Thank you for sharing this and those links. It would be useful to build a quantitative and qualitative summary of how and when early interventions in childhood lead to long-term gains. You can have a positive effect later in life and still have decay (or growth, or constant, or a mix). In our case, we are particularly interested in terms of subjective wellbeing rather than income alone.
I am a bit rusty on Bayesian model comparison, but—translating from my frequentist knowledge—I think the question isn’t so much whether the model is simpler or not, but how much error adding a parameter reduce? Decay probably seems to fit the data better.
Any model with more degrees of freedom will always fit the data (that you have!) better. A decay model nests a constant effects model, because the decay parameter can be zero.