Quick Links Podcast Episode Casual Inference is a podcast on all things epidemiology, statistics, data science, causal inference, and public health. Sponsored by the American Journal of Epidemiology. As a guest on this episode, I discuss data science communication, the different challenges of causal analysis in industry versus academia, and much more.| Emily Riederer
Figure 1: And Nadab and Abihu, the sons of Aaron, each took his censer, put fire in it, added incense, and offered strange fire before the Lord, which He had not commanded them. Then fire went out from the Lord and devoured them, and they died before the Lord. Lev 10:1-2 Notes on committing to things, and the implications of that for cooperation. Relevant to multi-agent causality where agents make decisions, in the context of iterated games in multi-agent systems with applications to AI safe...| The Dan MacKinlay stable of variably-well-consider’d enterprises
Game theory and decision theory for lots of interacting agents| The Dan MacKinlay stable of variably-well-consider’d enterprises
Figure 1 I just ran into this area while trying to invent something similar myself, only to find I’m years too late. It’s an interesting analysis suited to relaxed or approximated causal modelling of causal interventions. It seems to formalise coarse-graining for causal models. We suspect that the notorious causal inference in LLMs might be built out of such things or understood in terms of them. 1 Causality in hierarchical systems A. Geiger, Ibeling, et al. (2024) seems to summarise SOT...| The Dan MacKinlay stable of variably-well-consider’d enterprises
Structural Causal Models (SCMs) and Do-Calculus are foundational to causal reasoning and inference. SCMs formalize causal relationships through mathematical ...| Rehan Guha -Portfolio & Blog
Last week, I enjoyed attending parts of the annual virtual Causal Data Science Meeting organized by researchers from Maastricht University, Netherlands, and Copenhagen Business School, Denmark. This has been one of my favorite virtual events since the first iteration in 2020, and I find it consistently highlights the best of the causal research community: brining together industry and academia with concise talks that are at once thought-provoking, theoretically well-grounded, yet thoroughly p...| Emily Riederer
Quick Links Abstract Slides Video Slides Video Video - Discussion Post - Causal Design Patterns Post - Causal Data Management Experimentation is a pillar of product data science and machine learning. But what can you do when experimentation is impractical, costly, risky to customer experience, or too slow to read the desired long-term results? While industry is often spoiled by their ability to AB test, the question of how to draw valid causal measurements from non-randomized data has long be...| Emily Riederer
Data strategy motivated by causal methods This post summarizes the final third of my talk at Data Science Salon NYC in June 2023. Please see the talk details for more content. Techniques of observational causal inference are becoming increasingly popular in industry as a complement to experimentation. Causal methods offer the promise of accelerating measurement agendas and facilitating the estimation of previously un-measurable targets by allowing analysts to extract causal insights from “f...| Emily Riederer
We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on writ...| Emily Riederer