#
treatment-effects
Here are 50 public repositories matching this topic...
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
-
Updated
Jul 30, 2021 - Jupyter Notebook
Must-read papers and resources related to causal inference and machine (deep) learning
representation-learning
causal-inference
treatment-effects
causal-models
counterfactual
randomized-controlled-trials
paper-list
heterogeneous-treatment-effects
causal-discovery
counterfactual-learning
estimating-treatment-effects
causal-learning
-
Updated
Jun 28, 2021
Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.
causal-inference
treatment-effects
graph-convolutional-networks
graph-neural-networks
causality-algorithms
causal-machine-learning
-
Updated
Jul 8, 2021 - Python
Open
Add data simulator
andrewtavis
commented
Mar 31, 2021
Adding a data simulator would be a positive addition to causeinfer in that it would allow users to more accurately check general accuracies and compare models.
Files to create:
- causeinfer.data.simulation.py
- examples/simulation.ipynb
- Adding the dataset to [examples/an_iterated_model_dataset_comparison.ipynb](https://github.com/andrewtavis/causeinfer/blob/main/examples/an_iterated_model_
wwiecek
commented
Jun 18, 2020
Currently I run some loocv and logit models with iter=500 but soon even this won't be enough. I could run and store outputs with iter = 5000
Could either store within package or at a stable online location (GitHub?)
duketemon
commented
Jun 3, 2019
Methods for subgroup identification / personalized medicine / individualized treatment rules
treatment-effects
precision-medicine
subgroup-identification
heterogeneity-of-treatment-effect
treatment-scoring
personalized-medicine
individualized-treatment-rules
-
Updated
May 31, 2021 - R
My collection of causal inference algorithms built on top of accessible, simple, out-of-the-box ML methods, aimed at being explainable and useful in the business context
data-science
machine-learning
statistics
causality
causal-inference
treatment-effects
uplift-modeling
-
Updated
Jan 24, 2021 - Python
Code for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.
-
Updated
Jan 18, 2019 - Python
Statistical inference and graphical procedures for RD designs using local polynomial and partitioning regression methods.
-
Updated
Jul 26, 2021 - Stata
Tidy methods for Bayesian treatment effect models
-
Updated
Jul 26, 2021 - R
machine-learning
variable-importance
targeted-learning
causal-inference
treatment-effects
stochastic-interventions
marginal-structural-models
-
Updated
Mar 13, 2021 - R
Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
-
Updated
Jul 31, 2021 - Python
machine-learning
statistics
variable-importance
targeted-learning
causal-inference
treatment-effects
stochastic-interventions
censored-data
robust-statistics
causal-effects
stochastic-treatment-regimes
-
Updated
Jul 7, 2021 - R
This repository provides R-code for the estimation of the conditional average treatment effect (CATE) using machine learning (ML) methods.
machine-learning
causal-inference
treatment-effects
simulation-study
meta-learner
generalized-random-forest
-
Updated
May 17, 2021 - R
machine-learning
mediation-analysis
targeted-learning
causal-inference
treatment-effects
stochastic-interventions
inverse-probability-weights
-
Updated
Jul 21, 2021 - R
Deep Treatment Learning (R)
-
Updated
Mar 16, 2021 - R
Implementation of neural network algorithm for estimation of heterogeneous treatment effects and propensity scores described in Farrell, Liang, and Misra (2021)
-
Updated
Jun 19, 2021 - Python
Multiple Responses Subgroup Identification
-
Updated
Mar 27, 2021 - C++
machine-learning
mediation-analysis
targeted-learning
causal-inference
treatment-effects
stochastic-interventions
inverse-probability-weights
-
Updated
May 12, 2021 - R
Gaines and Kuklinski (2011) Estimators for Hybrid Experiments
-
Updated
Apr 22, 2018 - R
Experimental React component for an interactive spinning text treatment
-
Updated
Mar 15, 2018 - JavaScript
-
Updated
Aug 17, 2018 - Python
Vaccine/treatment trial progress tracker for the SARS-nCOV-2 virus and COVID-19 research and clinical trials happening all over the world.
dashboard
reactjs
postgresql
clinical-trials
treatment-effects
vaccine
coronavirus
covid-19
sars-cov-2
-
Updated
Jun 11, 2021 - HTML
Finite-sample inference for RD designs using local randomization and related methods.
-
Updated
Jul 8, 2021 - Stata
Manipulation testing using local polynomial density methods.
-
Updated
Mar 6, 2021 - Stata
This is a demonstration of how we can implement Hirano-Imbens (2004) model for estimating Average Dose Response Function under Normally distributed continuous treatment.
-
Updated
Aug 27, 2020 - Jupyter Notebook
Regression Discontinuity Design Software Packages
-
Updated
Jul 29, 2021 - CSS
Improve this page
Add a description, image, and links to the treatment-effects topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the treatment-effects topic, visit your repo's landing page and select "manage topics."
When you miss declaring a node in your causal graph, it's going to throw a
KeyError: 'label'error. It could be more explicit to make debugging easier. I think it would be nice to inform what is the node hough used in the graph.