The SUTVA, Positivity, Identifiability, Consistency, Exchangeability of Causal Inference, the essential ingredients that helps us bring out the true flavor of the causal model. Here is my understanding of each assumptions (main course) with examples (side dish) and accompanied by simulation (paired with beverages). Bon Appétit!
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!