A python package with tools to perform causal inference

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  • Enter Causal Inference: What If, written by Miguel Hernán and Jamie Robins, a book committed solely to this broad topic.. Jamie Robins and I have written a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several disciplines or confined to technical articles.
  • ML beyond Curve Fitting: An Intro to Causal Inference and

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Matching Methods for Causal Inference: A Machine Learning

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  • Gokhan is a senior data scientist at Attest. He is also a member of the Quanteda Initiative, and a guest lecturer for the LSE summer school course Introduction to Data Science and Machine Learning. As a computational social scientist, his core expertise lies in latent variable analysis, predictive modelling, and causal inference.
  • Summary of Causal Inference and Machine Learning
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Introduction To Causality — Causal Inference for the Brave

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  • Released our Python (Theano) code for learning Deep Markov Models; Video of our ICML 2016 tutorial on causal inference for observational studies Teaching Spring 2017, 2019, 2020: Machine Learning for Healthcare (6.871, HST.956) Fall 2017, 2018, 2019: Machine Learning (6.867) Fall 2016: Inference and Representation (DS-GA-1005 and CSCI-GA.2569)
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CausalML: Python Package for Causal Machine Learning

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  • Deep understanding of the theory of Machine Learning, Deep Learning, Computer Vision and Causal Inference; Experience in at least one Deep Learning framework such as Tensorflow, Pytorch and Programming Languages such as Python, Matlab, R and/or C/C++; Demonstrated project experience related to causal inference will be an advantage
  • His theoretical work has focused on developing machine learning and computational techniques for causal inference and econometric applications. Anastasopoulos has also worked as a machine learning scientist in the Python and R languages for governments and research organizations and has held academic positions at Princeton University, Harvard
  • The Seven Tools of Causal Inference with Reflections on Machine Learning • :3 down a mathematical equation for the obvious fact that “mud does not cause rain.” Even today, only the top echelon of the scientific community can write such an equation and formally …
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