Category: System 2 AI
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Causal Inference Study – Nov 20, 2023
What are the differences between SEM, Regression Analysis and ML? They all seem to estimate and predict the relationship between dependent variables and independent variables. Answer: They differ in multiple important aspects, such as scope, purpose, assumptions and output interpretation. Scope: SEM is a general modeling framework encompassing multiple multivariate techniques, while Regression Analysis is…
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Causal Inference Study – Nov 19, 2023
There is a section devoted to Structural Equation Models (SEMs) in Peter Spirtes’ paper, but he didn’t seem to explain what Structural Equation Modeling actually are. What is Structural Equation Modeling? My answer: Unlike Causal Bayesian Networks, which can be defined by their parts (i.e., G, P and the local directed Markov condition), Structural Equation…
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Causal Inference Study – Nov 17, 2023
Note 1 A Bayesian Network isn’t just a DAG, which I am often confused about, but a combination of two entities satisfying a relational condition. I can write it out as such: (G is a DAG and P is a probability density) <G, P> where P satisfies the local directed markov condition for G One…
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Causal Inference Study – Nov 16, 2023
Why is Causal Faithfulness Assumption a simplicity assumption? My answer: because to construct a model under no such assumption, one needs to consider the observed accidental independent relationships while construction of models under the simplicity assumption doesn’t. Critique: my answer is on the right track but it lacks details. It shows that my understanding of…
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Nov 10 & 14, 2023 Study – causal inference
Reading https://medium.com/data-science-at-microsoft/causal-inference-part-1-of-3-understanding-the-fundamentals-816f4723e54a What that means is that the question of causality comes down to comparing actual outcomes with counterfactual outcomes. Causal inference methods employ various assumptions to let us estimate the unobservable counterfactual outcome. By doing this, we can use them to make the appropriate comparison and estimate the treatment effect. … counterfactual prediction To identify…
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Causal Inference Study – Nov 8, 9 & 14, 2023
The narrower question focusing on causal inference is how it helps uncover causal relationships among variables and create applications in a particular problem domain? This is what this UofT causal inference workshop I will attend is about. My answer (not tested, based on my reading of spirtes’ paper): traditional causal inference provides the framework –…
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Nov 6, 2023 Study
The new project I am working on at the defect coaching team requires a model-generated seller list, ranked by ROI. The model used for this job is a tree-based algorithm on https://github.com/uber/causalml. As I am studying this paper on Causal Inference https://www.jmlr.org/papers/volume11/spirtes10a/spirtes10a.pdf, the two models for Causal Inference are Causal Bayesian Networks and Structural Equation…
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Causality
CausalML leads to better OOD generalization. The canonical representation of causal relationship is a causal directed acyclic graph, also called a causal diagram. It can encode a priori assumptions about the causal structure of interest. The definition of Bayesian networks is a probabilistic graphical model representing probabilistic relationships between random variables. One reason why graphs…
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Seeds for research
I adopted the concept of seeds from the book, the creative act by Rick Rubin here to document all interesting ideas that I want to pursue scientifically: (they will be organized into blog posts) Most seeds above come from https://yoshuabengio.org/2023/03/21/scaling-in-the-service-of-reasoning-model-based-ml/ -> GFlowNet is a manifestation of all these ideas. More seeds
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