Gang Fang's Blog

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…

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…

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…

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 –…

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…