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The article outlines two approaches to causal AI > There are two approaches to causal AI that are based on long-known principles: the potential outcomes framew
by memexy 6y ago
The article outlines two approaches to causal AI
> There are two approaches to causal AI that are based on long-known principles: the potential outcomes framework and causal graph models. Both approaches make it possible to test the effects of a potential intervention using real-world data. What makes them AI are the powerful underlying algorithms used to reveal the causal patterns in large data sets. But they differ in the number of potential causes that they can test for.
Does anyone have references and tutorials for either approach?
- pougetj 6y agoImbens and Rubin’s book “Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction” is an excellent reference for the Potential Outcomes approach. There are also several summaries online done by Rubin which do a great job of explaining the core concepts and how they apply in concrete examples. Rubin’s class in grad school was a large factor in my decision to focus my PhD on Causal Inference, and that book is one I return to frequently.
- memexy 6y agoThanks.
- Pils 6y agoSomeone posted this flowchart in a previous HN thread on Causal Inference frameworks: https://www.bradyneal.com/which-causal-inference-book https://www.bradyneal.com/which-causal-inference-book (I picked up Counterfactuals and Causal Inference and Elements of Causal Inference, and would recommend both).
- memexy 6y agoThanks.
- TudorBirlea 6y agoI suggest a different approach: we use Plan Analysis (Caspar/Grawe). This is a neuropsychotherapy instrument that can be used to actually map behavioural causality. These models here assume that you know causes for behaviour, and you only have to map them. BS. The biggest problem with an AI designed as such is that it doesn't have CONTEXT. Behaviour is never 'if A then B'.