Alternatives and similar repositories for propensity-score-matching

Users that are interested in propensity-score-matching are comparing it to the libraries listed below

• causal-machine-learning / kdd2021-tutorial
  EconML/CausalML KDD 2021 Tutorial
  ☆166Updated 2 years ago
• rlirey / psmatching
  Propensity score matching in Python 3
  ☆27Updated 2 years ago
• benmiroglio / pymatch
  ☆290Updated 2 years ago
• edgeslab / CTL
  A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python
  ☆67Updated last year
• kellieotto / pscore_match
  A Python package for propensity score matching
  ☆54Updated 4 years ago
• ijmbarr / notes-on-causal-inference
  Some notes on Causal Inference, with examples in python
  ☆154Updated 5 years ago
• OscarEngelbrektson / SyntheticControlMethods
  A Python package for causal inference using Synthetic Controls
  ☆192Updated last year
• 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…
  ☆340Updated last year
• laurencium / Causalinference
  Causal Inference in Python
  ☆573Updated 6 months ago
• IBM-HRL-MLHLS / IBM-Causal-Inference-Benchmarking-Framework
  Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring code
  ☆84Updated 7 years ago
• Ibotta / mr_uplift
  Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and mu…
  ☆72Updated 7 months ago
• Minyus / causallift
  CausalLift: Python package for causality-based Uplift Modeling in real-world business
  ☆350Updated 2 years ago
• inovex / justcause
  💊 Comparing causality methods in a fair and just way.
  ☆141Updated 5 years ago
• AliciaCurth / CATENets
  Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
  ☆153Updated last year
• kjung / scikit-learn
  A version of scikit-learn that includes implementations of Wager & Athey and Scott Powers causal forests.
  ☆22Updated 9 years ago
• timmens / causal-forest
  Implements the Causal Forest algorithm formulated in Athey and Wager (2018).
  ☆70Updated 5 years ago
• DoubleML / doubleml-for-py
  DoubleML - Double Machine Learning in Python
  ☆687Updated last week
• Chrisejorge / Causal-Inference
  Materials Collection for Causal Inference
  ☆47Updated 2 years ago
• jhartford / DeepIV
  Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction
  ☆161Updated 4 years ago
• MLResearchAtOSRAM / cause2e
  The cause2e package provides tools for performing an end-to-end causal analysis of your data. Developed by Daniel Grünbaum (@dg46).
  ☆63Updated 7 months ago
• amit-sharma / causal-inference-tutorial
  Repository with code and slides for a tutorial on causal inference.
  ☆583Updated 6 years ago
• d909b / drnet
  💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatm…
  ☆93Updated 2 years ago
• microsoft / SparseSC
  Fit Sparse Synthetic Control Models in Python
  ☆87Updated last year
• matteocourthoud / Blog-Posts
  Code and notebooks for my Medium blog posts
  ☆131Updated last year
• claudiashi57 / dragonnet
  ☆287Updated 3 years ago
• larsroemheld / causalinf_ex_elasticity
  A full example for causal inference on real-world retail data, for elasticity estimation
  ☆52Updated 4 years ago
• anurag-code / Survival-Analysis-Intuition-Implementation-in-Python
  Quick Implementation in python
  ☆53Updated 6 years ago
• jroessler / autoum
  A Python Framework for Automatically Evaluating various Uplift Modeling Algorithms to Estimate Individual Treatment Effects
  ☆23Updated 2 years ago
• suinleelab / treeexplainer-study
  Code and documentation for experiments in the TreeExplainer paper
  ☆189Updated 6 years ago
• julianspaeth / random-survival-forest
  A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.
  ☆64Updated last year