Summary of UCL Seminar - What is Causal AI

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00:00:00 - 01:00:00

This video discusses the concept of causal AI and how it can be used to make better decisions. The speaker explains how current deep learning and machine learning approaches are flawed and how a more methodical approach is needed. The video concludes by discussing where causal AI is headed and how interested they are in the finance world.

00:00:00 The speaker discusses how causal AI is important and how it can be used to make better decisions. He goes on to describe how current deep learning and machine learning approaches are flawed and how a more methodical approach is needed. The speaker concludes by discussing where causal AI is headed and how interested they are in the finance world.
00:05:00 This video discusses how to estimate the treatment effect or causal effect in the causality space, using a technique called instrumental variables. A Nobel Prize was awarded to economists for their work in this area, which focuses on estimating the effects of different government policies on citizens.
00:10:00 The video discusses how classical statistics can be mislead by spurious correlations, or correlations between variables that have no real connection to one another. It explains how to adjust for confounding variables in order to determine the true effect of a treatment. Finally, it explains how age can be a confounding variable in a study of the effect of a vaccine.
00:15:00 The presenter talks about the dangers of correlations between variables in data, and how traditional methods for machine learning (such as deep neural networks) can be overfitted and fail when applied to data outside of the training set. They go on to discuss post hoc explanations, which can be difficult to interpret and can lead to incorrect decisions.
00:20:00 The causal model is a model that tries to learn the true data generating process from data. This can be done by removing spurious correlations, modeling the true data generating process, and generalizing to unseen data. This is important because it allows for more trustworthy and fair decisions.
00:25:00 The video discusses how causal discovery algorithms work, and how they rely on assumptions about the data. It then provides an example of how an asymmetry in scatterplots can help determine which direction an arrow points. The video also discusses how causal discovery can be incomplete, and how it is always reliant on human assumptions.
00:30:00 The video discusses some of the different methods used to discover causal relationships in data. One such method is the pc algorithm, which relies on detecting conditional dependencies between variables. This is a difficult task, as the number of variables grows exponentially with the number of nodes in the graph. Another method is constraint-based, in which the analyst tries to orient edges between nodes so that they are as independent as possible. However, even with a perfect oracle, the analyst may not be able to converge on a single graph.
00:35:00 This 1-minute video provides a brief overview of causal AI, which is a field of AI that aims to imbue methods with domain knowledge so they can make better decisions. There are many methods used to estimate treatment effects, but they all fall under the umbrella of calculus. One example of a structural causal model is a directed acyclic graph with functional dependencies on edges. This model provides an underlying dag that estimates the observational distribution in traditional machine learning models.
00:40:00 The Structural Causal Model allows for the estimation of intervention distributions and the performance of counterfactuals, which is useful for understanding the effects of different interventions.
00:45:00 This 1-paragraph summary describes the video "UCL Seminar - What is Causal AI" and its main points. The video discusses the concept of causal AI, which is a type of artificial intelligence that uses causal mechanisms to make decisions. The video explains how causal AI is important for future regulation of consumer products, such as mortgages.
00:50:00 In this lecture, Andrew Shaw explains how causal models can be used to more accurately predict what will happen in the real world. He also mentions a method called multicore linearity solving, which can be used to identify and solve causal problems quickly.
00:55:00 The speaker provides an overview of various methods for causal inference, including vector autoregressive models (VARs), no tiers models, and lingam. He notes that while the literature on this topic is sparse, it appears that data-driven approaches are less likely to converge on the true causal structure.

01:00:00 - 01:05:00

This YouTube video is a seminar discussing causal AI. The presenter discusses how causal AI is different from traditional AI, how it can be used to solve various problems, and some of the techniques used in causal AI, including deep neural networks and cross validation.