Code for paper M-estimation: a worked example with connections to maximum likelihood estimation

Fusing Trial Data for Treatment Comparisons: Single versus Multi-Span Bridging

Tutorial_Computational_Causal_Inference_Estimators

Ensemble Learning Targeted Maximum Likelihood for Stata users

Introduction to the mosts common estimators and computation in causal inference for epidemiologists: A tutorial

Noisy network measurement with stan

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Generalized Linear Models in Sklearn Style

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

Bayesian Additive Regression Trees For Python

Simulation code and results for https://arxiv.org/abs/1705.08527

Targeted Maximum Likelihood Estimation for Network Data

Python-based API-Wrapper to access Scopus

Network Analysis in Python

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

Statsmodels: statistical modeling and econometrics in Python

An R package for causal inference with interference ('inferference')

Implements "stabilized" IPW estimators under interference from Liu et al. 2016 Biometrika https://doi.org/10.1093/biomet/asw047

LaTeX style for Python highlighting

Code for paper: "The parametric G-formula for time-to-event data: towards intuition with a worked example"

Drawing graphical models for causal inference using LaTeX

An implementation of the SuperLearner algorithm in Python based on scikit-learn. (py3 compatible)

A game theoretic approach to explain the output of any machine learning model.