R companion to Angrist Pischke Mostly Harmless econometrics
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Updated
Oct 4, 2015 - R
R companion to Angrist Pischke Mostly Harmless econometrics
Working repository for Causal Tree and extensions
Targeted Learning entry in the Atlantic Causal Inference Conference's 2017 competition
Quantifying the impact of a documentary on KeepCup search volume
💬 Talk on "Fair Inference on Outcomes" (R. Nabi & I. Shpitser, 2017), for M. Hardt's "Fairness in Machine Learning" seminar at Berkeley, Fall 2017
Simple implementation for estimating causal effects with M-estimation and sandwich variance estimators
Evaluating BART and Synthetic Tree-Based Methods for the Estimation of Individual Causal Effects, final project for CM764 - Statistical Learning - Function Estimation at uWaterloo
Resources for research on the ability of causal inference algorithms to learn a model of a distributed Jenkins cluster hosted on AWS
Code to accompany and provide results for "Causal Queries from Observational Data in Biological Systems with Bayesian Networks: An Empirical Study in Small Networks"
Machine Learning 2016/2017 - MSc Artificial Intelligence @ UvA
R/tstmle01: Estimation and Inference for Marginal Causal Effect with Single Binary Time Series
Generalized Random Forests
The Inductive Causation and IC* algorithms applied to a fake data set
Course website for "Targeted Learning in Biomedical Big Data" (Spring 2018, UC Berkeley)
Causal discovery from mixed data with missing values.
Projects from the Course on "Algorithms for Data guided Business Intelligence"
💬 Talk on "Sensitivity Analysis for Inverse Probability Weighting Estimators via the Percentile Bootstrap" (Q. Zhao et al., 2017), for S. Pimentel's "Observational Study Design and Causal Inference" seminar at Berkeley, Spring 2018
This is the replication of one R tutorial introduced in Machine Learning and Econometrics tutorial in AEA Annual Meeting 2018.
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