Topic: treatment-effects Goto Github
Some thing interesting about treatment-effects
Some thing interesting about treatment-effects
treatment-effects,Experimental React component for an interactive spinning text treatment
Organization: 100shapes
treatment-effects,Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
User: aliciacurth
treatment-effects,Machine learning based causal inference/uplift in Python
User: andrewtavis
treatment-effects,Quantifying the Persistence of Misinformation: A Case Study in R
User: annacalla
treatment-effects,Course of Causal Inference at the Faculty of Economics of Lomonosov Moscow State University (In Russian)
User: annastavniychuk
Home Page: https://annastavniychuk.github.io/AppliedEconometricsMSU/
treatment-effects,Review: Data-driven methodology for detecting treatment effect heterogeneity
User: ashwinikv
treatment-effects,Bounding Treatment Effects by Pooling Limited Information across Observations
Organization: atbounds
treatment-effects,Multiple Responses Subgroup Identification
User: baconzhou
Home Page: https://baconzhou.github.io/MrSGUIDE/
treatment-effects,Tidy methods for Bayesian treatment effect models
User: bonstats
treatment-effects,
Organization: cancersysbio
treatment-effects,CRAN Task View: Causal Inference
Organization: cran-task-views
Home Page: https://CRAN.R-project.org/view=CausalInference
treatment-effects,Lightweight uplift modeling framework for Python
User: duketemon
Home Page: https://pyuplift.readthedocs.io
treatment-effects,
Organization: forestry-labs
treatment-effects,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
User: gdmarmerola
treatment-effects,Comparison of treatment effect in Randomized Control Trial (RCT) and Propensity Score Matching methods, conducted on Large-Scale Dataset by 'Criteo'.
User: giogioshvili
treatment-effects,Univariate conditional average treatment effect estimation for predictive biomarker discovery
Organization: insightsengineering
Home Page: https://insightsengineering.github.io/uniCATE/
treatment-effects,Methods for subgroup identification / personalized medicine / individualized treatment rules
User: jaredhuling
Home Page: http://jaredhuling.org/personalized/
treatment-effects,Must-read papers and resources related to causal inference and machine (deep) learning
User: jvpoulos
treatment-effects,Implementation of paper DESCN, which is accepted in SIGKDD 2022.
User: kailiang-zhong
treatment-effects,IPW- and CBPS-type propensity score reweighting, with various extensions (Stata package)
User: kkranker
Home Page: https://ideas.repec.org/c/boc/bocode/s458657.html
treatment-effects,Adaptive debiased machine learning of treatment effects with the highly adaptive lasso
User: larsvanderlaan
Home Page: https://arxiv.org/abs/2307.12544
treatment-effects,Gaines and Kuklinski (2011) Estimators for Hybrid Experiments
User: leeper
Home Page: https://cloud.r-project.org/package=GK2011
treatment-effects,Accounting for hidden confounders in estimates of dose-response curves from observational data.
User: marmarelis
treatment-effects,A modified uplift modeling technique to convert "treatment nonresponders" to "responders" is proposed through multifaceted interventions in market campaigns.
User: mazba-ahamad
treatment-effects,This is a demonstration of how we can implement Hirano-Imbens (2004) model for estimating Average Dose Response Function under Normally distributed continuous treatment.
User: mursaleenshiraj
treatment-effects,Vaccine/treatment trial progress tracker for the SARS-nCOV-2 virus and COVID-19 research and clinical trials happening all over the world.
Organization: ncov19-us
Home Page: https://vaccine.ncov19.us
treatment-effects,:package: R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects
User: nhejazi
Home Page: https://codex.nimahejazi.org/medoutcon
treatment-effects,:package: :game_die: R/medshift: Causal Mediation Analysis for Stochastic Interventions
User: nhejazi
Home Page: https://codex.nimahejazi.org/medshift
treatment-effects,:package: :game_die: R/txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions, with Corrections for Outcome-Dependent Sampling
User: nhejazi
Home Page: https://codex.nimahejazi.org/txshift
treatment-effects,OpenASCE (Open All-Scale Casual Engine) is a Python package for end-to-end large-scale causal learning. It provides causal discovery, causal effect estimation and attribution algorithms all in one package.
Organization: open-all-scale-causal-engine
Home Page: https://openasce.openfinai.org/en-US
treatment-effects,Implementation of neural network algorithm for estimation of heterogeneous treatment effects and propensity scores described in Farrell, Liang, and Misra (2021)
User: popovicmilica
treatment-effects,DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Organization: py-why
Home Page: https://www.pywhy.org/dowhy
treatment-effects,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.
Organization: py-why
Home Page: https://www.microsoft.com/en-us/research/project/alice/
treatment-effects,Manipulation testing using local polynomial density methods.
Organization: rdpackages
Home Page: https://rdpackages.github.io/rddensity/
treatment-effects,Finite-sample inference for RD designs using local randomization and related methods.
Organization: rdpackages
treatment-effects,Estimation, inference, RD Plots, and extrapolation with multiple cutoffs and multiple scores RD designs.
Organization: rdpackages
treatment-effects,Regression Discontinuity Design Software Packages
Organization: rdpackages
Home Page: https://rdpackages.github.io
treatment-effects,Power and sample size calculations for RD designs using robust bias-corrected local polynomial inference.
Organization: rdpackages
treatment-effects,Statistical inference and graphical procedures for RD designs using local polynomial and partitioning regression methods.
Organization: rdpackages
treatment-effects,Code for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.
User: rguo12
treatment-effects,Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.
User: rguo12
treatment-effects,Treatment Effect Heterogeneity visualization using R
User: sanoke
treatment-effects,Deep Treatment Learning (R)
User: skadieye
treatment-effects,Causal Inference in Case-Control Studies
User: sokbae
Home Page: https://CRAN.R-project.org/package=ciccr
treatment-effects,BITES: Balanced Individual Treatment Effect for Survival data
User: sschrod
treatment-effects,A General Causal Inference Framework by Encoding Generative Modeling
Organization: suwonglab
Home Page: https://causalegm.readthedocs.io/
treatment-effects,Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
Organization: tlverse
Home Page: https://tlverse.org/causalglm/
treatment-effects, 🎯 :twisted_rightwards_arrows: Targeted Learning for Causal Mediation Analysis
Organization: tlverse
Home Page: https://tlverse.org/tmle3mediate
treatment-effects, 🎯 :game_die: Targeted Learning of the Causal Effects of Stochastic Interventions
Organization: tlverse
Home Page: https://tlverse.org/tmle3shift
treatment-effects,R package for Bayesian meta-analysis models, using Stan
User: wwiecek
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