Yongnam Kim
@ykims.bsky.social
Education researcher interested in causal inference & DAGs | Seoul National University
Pinned
Yongnam Kim
@ykims.bsky.social
· Jan 19
Clear from the DAG, A² acts as an instrumental variable (conditional on A), enabling the identification of the M → Y effect even with U. This is what shorturl.at/1TgCm showed: mediation analysis can be valid (even with U) if the M model has a higher order of A than the Y model.
What does “unconditional” really mean? P(data) seems unconditional, and P(data | boys) conditional. But imagine an alien landing on Earth and seeing P(data). It says, “Oh, so you’re conditioning on humans, not tigers.” Every “unconditional” is just conditional on a world we take for granted.
October 22, 2025 at 3:14 PM
What does “unconditional” really mean? P(data) seems unconditional, and P(data | boys) conditional. But imagine an alien landing on Earth and seeing P(data). It says, “Oh, so you’re conditioning on humans, not tigers.” Every “unconditional” is just conditional on a world we take for granted.
We’re too obsessed with decomposing direct and indirect effects in mediation. "mediation should not be understood in terms of decomposition...Once the priority of research questions is established, the practical irrelevance of statistical effect decomposition directly follows" osf.io/preprints/ps...
OSF
osf.io
May 16, 2025 at 4:47 AM
We’re too obsessed with decomposing direct and indirect effects in mediation. "mediation should not be understood in terms of decomposition...Once the priority of research questions is established, the practical irrelevance of statistical effect decomposition directly follows" osf.io/preprints/ps...
Card & Krueger's (1994) minimum wage study may be such an extreme case of confounding: "State" (NJ vs. PA), a confounder, perfectly correlates with the causal variable "minimum wage." Their interest was in the effect of minimum wage on employment, not the effect of restaurants' state location.
May 1, 2025 at 1:18 AM
Card & Krueger's (1994) minimum wage study may be such an extreme case of confounding: "State" (NJ vs. PA), a confounder, perfectly correlates with the causal variable "minimum wage." Their interest was in the effect of minimum wage on employment, not the effect of restaurants' state location.
Reposted by Yongnam Kim
Looking for a tool to more easily draw your DAGs and reason on them? Try PV-dagger (pvverse.github.io/pv_dagger/). Specifically designed by @fusarolimichele.bsky.social to deal with the complex DAGs involved in pharmacovigilance, helps positioning and color-coding confounds, measurement errors, etc
Visualization of Causal Structures in Pharmacovigilance Data Using DAGs
The PVdagger package provides tools for creating and visualizing Directed Acyclic Graphs (DAGs) with various biases and paths. This package is particularly useful for researchers and signal managers i...
pvverse.github.io
February 7, 2025 at 11:46 AM
Looking for a tool to more easily draw your DAGs and reason on them? Try PV-dagger (pvverse.github.io/pv_dagger/). Specifically designed by @fusarolimichele.bsky.social to deal with the complex DAGs involved in pharmacovigilance, helps positioning and color-coding confounds, measurement errors, etc
A key insight is the equivalence btw suppressors and instrumental variables. Yes, DAGs are useful for understanding why S is zero-related with Y, yet can increase the overall prediction.
Interested in suppressor variables after the episode of @quantitude.bsky.social ?
This paper from @ykims.bsky.social uses DAGs to easily explain them and shows how suppressor variables could even be used to bypass confounding!
journals.sagepub.com/doi/pdf/10.3...
This paper from @ykims.bsky.social uses DAGs to easily explain them and shows how suppressor variables could even be used to bypass confounding!
journals.sagepub.com/doi/pdf/10.3...
January 27, 2025 at 7:14 PM
A key insight is the equivalence btw suppressors and instrumental variables. Yes, DAGs are useful for understanding why S is zero-related with Y, yet can increase the overall prediction.
Reposted by Yongnam Kim
Reposted by Yongnam Kim
This sounds like the same error I blogged about a few years ago, the common error of trying to control for population (or body size or many etc) by dividing the outcome variable by it. Props to the authors for seeking review and taking the issue seriously. Role models for us all.
January 21, 2025 at 7:38 AM
This sounds like the same error I blogged about a few years ago, the common error of trying to control for population (or body size or many etc) by dividing the outcome variable by it. Props to the authors for seeking review and taking the issue seriously. Role models for us all.
Clear from the DAG, A² acts as an instrumental variable (conditional on A), enabling the identification of the M → Y effect even with U. This is what shorturl.at/1TgCm showed: mediation analysis can be valid (even with U) if the M model has a higher order of A than the Y model.
January 19, 2025 at 5:00 AM
Clear from the DAG, A² acts as an instrumental variable (conditional on A), enabling the identification of the M → Y effect even with U. This is what shorturl.at/1TgCm showed: mediation analysis can be valid (even with U) if the M model has a higher order of A than the Y model.
DAGs (causal graphs) can be used to understand the mechanics of linear interaction analysis. See more here: bpspsychub.onlinelibrary.wiley.com/doi/10.1111/...
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...
bpspsychub.onlinelibrary.wiley.com
December 2, 2024 at 2:02 AM
DAGs (causal graphs) can be used to understand the mechanics of linear interaction analysis. See more here: bpspsychub.onlinelibrary.wiley.com/doi/10.1111/...