Simulating a binary dataset, coupled with an understanding of the logit link and the linear formula, is truly fascinating! However, we must exercise caution regarding our adjustments, as they can potentially divert us from the true findings. I advocate for transparency in Directed Acyclic Graphs (DAGs) and emphasize the sequence: causal model -> estimator -> estimand.
Saving can be enjoyable! If you’re planning to cut down on takeout orders, why not use past data to simulate your savings? Let it inspire and motivate your future dining-in decisions! 👍
Beware of what we adjust. As we have demonstrated, adjusting for a collider variable can lead to a false estimate in your analysis. If a collider is included in your model, relying solely on AIC/BIC for model selection may provide misleading results and give you a false sense of achievement.
Front-door adjustment: a superhero method for handling unobserved confounding by using mediators (if present) to estimate causal effects accurately
I have learned a tremendous amount from everyone involved, including members from other teams. The entire experience has been truly enriching, and I thoroughly enjoyed every moment of it. I want to express my heartfelt gratitude to everyone involved for making this an exceptional learning journey. Thank you all!