If experimenters regularly thought like modelers and actually did use synthetic data to vet their designs, they'd be running orders of magnitudes fewer experiments and the literature wouldn't be so saturated with empirical garbage.

This is my go to https://app.diagrams.net

Regurgitation, get it published and call it a day..🤡

Despite this great advise, as a newbie I have gone with high risk high reward stocks and got burnt badly. But I don’t regret because I learned a lot! That was my burn-in period (MCMC analogy).

The advice I got from my PhD advisor when I started the program was: consider your research career like a stock portfolio. Have a healthy mix of high risk high reward stocks along with some mutual fund stocks that are low risk, low reward.

💯 agree! You need to be really strong from within to be vulnerable. But the people who just focus on showing “strength” doesn’t seem to understand this. Sometimes those are the people who are the most insecure inside.

Them sharing their experience is going to have a tremendous impact on my career & personal life, because it changed how I look at it and how I want to tackle it. What makes a company great place to work is people like this, not free snacks or ping-pong tables!

After all, we are not just a group of robots; we are human beings with emotions and vulnerabilities. Embracing this openness can foster a more supportive and understanding environment for everyone.

Welcome Dr. Roschelle!

I would also throw the following papers to the mix, for a quick birds eye view understanding:
1. Causal Machine Learning: Survey & Open Problems.
2. Potential Outcomes & DAG Approaches to Causality by Imbens
3. Causal Deep Learning.