Yongnam Kim
ykims.bsky.social
Yongnam Kim
@ykims.bsky.social
Education researcher interested in causal inference & DAGs | Seoul National University
A bit late, but you might find this interesting, osf.io/preprints/ps.... I think we have the same graph about Lord’s paradox.
OSF
osf.io
December 26, 2025 at 10:00 AM
This leads to an embarrassing thought: what I draw in my DAGs might itself be the result of a collider in some meta-DAG of the universe. I drew Sex → Weight and was so sure of the structure. But in a higher-order universe, this might itself be the result of collider conditioning.
October 22, 2025 at 9:51 PM
a fun part is, these two approaches might give conflicting results about the effect of T. I think this can be another version of Lord's paradox.
May 2, 2025 at 1:32 AM
I think your approach is ok. You just defined your question as the effect of T on Y/X, and there’s nothing wrong with it. But it might be good to think about why you're using Y/X. If you want to account for the role of X, another option is Y~T+X, which gives the effect of T on Y holding X constant.
May 2, 2025 at 1:28 AM
Card & Krueger’s minimum wage study may be a real example of a positivity violation. Their DiD addresses positivity, not unconfoundedness.
osf.io/preprints/ps...
OSF
osf.io
January 25, 2025 at 10:57 AM
Why HIGHER? If not, A² also be part of the Y model, implying A² → Y, which violates the exclusion restriction. This shows why the DAG representation suggested in shorturl.at/Tj8am is useful. A² = A × A can be described in DAGs, offering intuition for analysis mechanics.
British Journal of Mathematical and Statistical Psychology | Wiley Online Library
Interaction analysis using linear regression is widely employed in psychology and related fields, yet it often induces confusion among applied researchers and students. This paper aims to address thi....
shorturl.at
January 19, 2025 at 5:01 AM
Very happy to share this final version with you. Thank you! ;-)
December 2, 2024 at 7:59 AM
Easy to see why the cor btw the first-order and interaction terms (indicating collinearity) after centering becomes zero (though this is not the reason for centering); why centering X1 only (not X2) change the coef on X2​ while leaving the coefs on X1 and the (centered) interaction term unchanged.
December 2, 2024 at 2:07 AM