Alternatives and similar repositories for Double-ML-Continuous-Treatment

Users that are interested in Double-ML-Continuous-Treatment are comparing it to the libraries listed below

• victor5as / RieszLearning
  Replication files for Chernozhukov, Newey, Quintas-Martínez and Syrgkanis (2021) "RieszNet and ForestRiesz: Automatic Debiased Machine Le…
  ☆14Updated 3 years ago
• xnie / rlearner
  Quasi-Oracle Estimation of Heterogeneous Treatment Effects
  ☆114Updated 4 years ago
• calebleedy / ISU-Causal
  Repository for the ISU Causal Inference Working Group
  ☆12Updated last year
• ainaimi / mlci_shortcourse
  ☆13Updated last year
• soerenkuenzel / causalToolbox
  ☆42Updated 4 years ago
• ehkennedy / npcausal
  ☆96Updated last year
• grf-labs / policytree
  Policy learning via doubly robust empirical welfare maximization over trees
  ☆83Updated last month
• kalinlau / awesome-causal-learning
  Causality with machine learning, topic including causal represenation learning, causal reinforcement learning
  ☆11Updated 4 years ago
• vdorie / aciccomp
  Code to run submissions for the Atlantic Causal Inference Competition
  ☆42Updated last year
• vdorie / npci
  Non-parametrics for Causal Inference
  ☆49Updated 3 years ago
• kochbj / Deep-Learning-for-Causal-Inference
  Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflo…
  ☆335Updated 10 months ago
• fbargaglistoffi / BCF-IV
  Package for heterogeneous causal effects in the presence of imperfect compliance (e.g., instrumental variables, fuzzy regression disconti…
  ☆17Updated last year
• anishazaveri / austen_plots
  Demo data and code for "Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding".
  ☆26Updated 3 years ago
• CausalAIBook / MetricsMLNotebooks
  Notebooks for Applied Causal Inference Powered by ML and AI
  ☆129Updated 5 months ago
• AliciaCurth / CATENets
  Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
  ☆143Updated last year
• callmespring / CausalRL
  Implementation of "Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework" (JASA 2023)
  ☆32Updated last year
• swager / balanceHD
  Approximately balanced estimation of average treatment effects in high dimensions.
  ☆34Updated 4 years ago
• carloscinelli / dml.sensemakr
  Sensitivity analysis tools for causal ML
  ☆18Updated last month
• EddieYang211 / ebal-py
  Python implementation of Entropy Balancing for binary and continuous treatment
  ☆20Updated 3 years ago
• forestry-labs / causalToolbox
  ☆44Updated 2 years ago
• MCKnaus / causalDML
  Implementation of Double Machine Learning
  ☆36Updated 4 months ago
• DoubleML / doubleml-docs
  Documentation and User Guide for DoubleML - Double Machine Learning in Python & R
  ☆24Updated 2 weeks ago
• CausalInference / pygformula
  The pygformula implements the parametric g-formula in Python. The parametric g-formula (Robins, 1986) uses longitudinal data with time-va…
  ☆30Updated 5 months ago
• sachsmc / causaloptim
  An Interface to Specify Causal Graphs and Compute Balke Bounds
  ☆22Updated 6 months ago
• d2cml-ai / 14.388_py
  This material has been created based on the tutorials of the course 14.388 Inference on Causal and Structural Parameters Using ML and AI …
  ☆16Updated 2 years ago
• jantonelli111 / DoublyRobustHD
  R package for doubly robust estimates of causal effects in high-dimensions using flexible Bayesian methods
  ☆26Updated 9 months ago
• lihualei71 / cfcausalPaper
  Paper Repository
  ☆11Updated 2 years ago
• vsemenova / orthoml
  Code associated with paper: Orthogonal Machine Learning for Demand Estimation: High-Dimensional Causal Inference in Dynamic Panels, Seme…
  ☆25Updated 2 years ago
• mwelz / GenericML
  R implementation of Generic Machine Learning Inference (Chernozhukov, Demirer, Duflo and Fernández-Val, 2020).
  ☆70Updated 7 months ago
• boschresearch / causalAssembly
  ☆30Updated last week