Titlebook: Cause Effect Pairs in Machine Learning; Isabelle Guyon,Alexander Statnikov,Berna Bakir Bat Book 2019 Springer Nature Switzerland AG 2019 C

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Learning Bivariate Functional Causal Models. and .?→?.. In this chapter, we first define what is meant by generative modeling and what are the main assumptions usually invoked in the literature in this bivariate setting. Then we present the theoretical identifiability problem that arises when considering causal graph with only two variables.

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Discriminant Learning Machines trained from data. This can be thought of as a kind of meta learning. This chapter will present an overview of the contributions in this domain and state the advantages and limitations of the method as well as recent theoretical results (learning theory/mother distribution). This chapter will point

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Results of the Cause-Effect Pair Challengehe participants were provided with a large database of thousands of pairs of variables {., .?} (80% semi-artificial data and 20% real data) from which samples were drawn independently (i.e. ignoring possible time dependencies). The goal was to discover whether the data supports the hypothesis that .

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Non-linear Causal Inference Using Gaussianity Measuresels contaminated with additive non-Gaussian noise. Assuming that the causes and the effects have the same distribution, we show that the distribution of the residuals of a linear fit in the anti-causal direction is closer to a Gaussian than the distribution of the residuals in the causal direction.

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From Dependency to Causality: A Machine Learning Approachhe ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches. This paper proposes a supervised machine learning approach to infer the existence of a directed causal li

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