Naftali Weinberger

Selected papers Timescale: tinyurl.com/2rpj7874 DCMs: tinyurl.com/2s3p2wuh Discrimination: tinyurl.com/yscv658e Mediation: tinyurl.com/3hz3rca8 Faithfulness: tinyurl.com/3n3de87m Causation and Time: tinyurl.com/58cfcw2r Psychometrics: tinyurl.com/ynjaf5xv

In the newest episode, I consider the debate over whether demographic variables such as race and gender are causes. In it, I clarify what's at stake, and what the answer does (and doesn't) depend on. youtu.be/UzpReyvtKDU?si…

I am delighted to introduce 6 wonderful Harris PhD students who are on the job market this year. Claire Fan estimates the economic costs of hydro dams in foreign downstream countries. She finds that despite the lack of a central international authority, countries with cooperative international relations can mitigate these externalities. claireqfan.github.io

A new episode on the Manipulation Theorem, which is not as well-known as it should be, but has hugely influenced philosophers. I cover hard vs soft interventions, why causation doesn't require human agency, and whether causation requires open systems. youtu.be/fg1ApidilVM

What does it mean to assign probabilities to causal parameters? In just one minute and 33 seconds, I explain why this is puzzling, and how you can nevertheless make sense of it. youtu.be/UMJSFkz2sCg

In the latest episode, I relate Bostrom's simulation argument to debates over the causal faithfulness condition. I get into interpretations of probability, why "coincidental"≠"improbable", and what it means to assign a probability to a causal parameter. youtu.be/9Jwrw-oSUFU

I've now posted an episode on the causal faithfulness condition. Faithfulness says much more than just that causal paths don't cancel, and raises foundational questions about causation, probability, and underdetermination. youtu.be/k8p0OS-8u2Q

A sneak peak for next week's episode: Does Causation Imply Correlation? youtu.be/5ocnRfA3PX8

I'm excited to post this new episode on the causal Markov condition! youtu.be/eQNfpaAURdU?si…

I just posted a video on the Front-Door criterion, which enables you to identify an effect using mechanisms. This video is more technical than the others, but really illustrates how causal graphs create a bridge between causes and probabilities. youtu.be/xRFOj71FPU0

After a bit of a summer pause, I'm back to making episodes. In this episode, I explain the notion of confounding, and clarify why confounders should not be thought of as alternate explanations of an observed effect. youtu.be/kAgS7cltBhM

@david_lagnado @BetsyOgburn might know something about this. She's been called to testify about evidential relevance in some recent court cases in the US.

@DAGophile Related to harmless error in USA law - and ‘unsafe’ convictions in UK

I decided to make an intro episode, to provide some orientation for the later episodes. In it, I introduce DAGs and explain how causation is a strategy for managing complexity. I spent a lot of time improving the sound quality, so I hope that paid off. youtube.com/watch?v=7h6tnV…

@david_lagnado I'd be surprised if this was very helpful, but it popped into my head: jstor.org/stable/2062005…

@DAGophile Thanks - I’m reading that paper! I’m actually looking for a measure of *actual* causal strength 😳which will be heavily context dependent. It’s for a new project I’ve started on assessing causal impact of different pieces of evidence on a jury/judge’s decision …

@david_lagnado ...it's always great to hear from you. I'd be game for a chat to catch up at some point.

@david_lagnado ...also, now that I'm learning more stats, I'm seeing that lots of the measurement issues about categorical vs continuous etc might interact in interesting ways with the causal side of things, though I'm at the beginning of the my thought process there...

This clip is the part of the last episode that has so far generated the most discussion. youtube.com/clip/UgkxeuvUM…

How can a drug raise the chance of recovery in a population, while lowering the chance in both men and women? The answer is Simpson's paradox, which still confuses people. Here I clarify it, w/ examples from COVID-19, and testing police discrimination. youtube.com/watch?v=MMnAWU…

Coming next week on Causal Foundations: Simpson's Paradox! Subscribe to my YouTube channel in order to not miss episodes when they're released. @dagophile" target="_blank" rel="nofollow noopener">youtube.com/@dagophile

Episode 3 is now up. In it, I discuss what the most underrated concept in causal inference: identifiability. It's what enables one to measure causal effects from data. I'm still figuring out the AV side of things. Feedback welcome! youtu.be/FABdstSaUA4