Alternatives and similar repositories for opossum:

Users that are interested in opossum are comparing it to the libraries listed below

• Ibotta / mr_uplift
  Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and mu…
  ☆66Updated 7 months ago
• andrewtavis / causeinfer
  Machine learning based causal inference/uplift in Python
  ☆59Updated last year
• FlorianWilhelm / bhm-at-scale
  🪜 Bayesian Hierarchical Models at Scale
  ☆52Updated 3 years ago
• edgeslab / CTL
  A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python
  ☆62Updated last year
• larsroemheld / causalinf_ex_elasticity
  A full example for causal inference on real-world retail data, for elasticity estimation
  ☆50Updated 3 years ago
• microsoft / SparseSC
  Fit Sparse Synthetic Control Models in Python
  ☆80Updated 11 months ago
• yaronshap / GloballyConsistentRules
  Implementation of algorithms from the paper "Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application…
  ☆24Updated 2 years ago
• inovex / justcause
  💊 Comparing causality methods in a fair and just way.
  ☆138Updated 5 years ago
• thequackdaddy / tweedie
  Tweedie family density estimation in python
  ☆28Updated last year
• 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
  ☆82Updated 6 years ago
• zalandoresearch / ACIC23-competition
  Data for and description of the ACIC 2023 data competition
  ☆32Updated last year
• timmens / causal-forest
  Implements the Causal Forest algorithm formulated in Athey and Wager (2018).
  ☆69Updated 4 years ago
• shansfolder / AB-Test-Early-Stopping
  Evaluation of early stopping algorithms in A/B testing
  ☆17Updated 7 years ago
• socket778 / XBCF
  R and python implementations of Accelerated Bayesian Causal Forest.
  ☆25Updated 8 months ago
• DanielhCarranza / causai
  Causai is a Python package for Causality in Machine Learning. We provide state-of-the-art causal algorithms and ML into decision-making s…
  ☆12Updated 4 years ago
• MasaAsami / pysynthdid
  Synthetic difference in differences for Python
  ☆76Updated 11 months ago
• franrruiz / shopper-src
  Code for Shopper, a probabilistic model of shopping baskets
  ☆52Updated 4 years ago
• kjung / scikit-learn
  A version of scikit-learn that includes implementations of Wager & Athey and Scott Powers causal forests.
  ☆22Updated 8 years ago
• google / empirical_calibration
  ☆44Updated 10 months ago
• PlaytikaOSS / uplift-analysis
  A Python library that implements scoring utilities, analysis strategies, and visualization methods which can serve uplift modeling use-ca…
  ☆24Updated 2 years ago
• bradyneal / causal-book-code
  ☆76Updated 4 years ago
• klostest / uplift_modeling
  ☆17Updated 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).
  ☆58Updated last year
• bernardodionisi / differences
  difference-in-differences in Python
  ☆100Updated last year
• d909b / drnet
  💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatm…
  ☆87Updated 2 years ago
• duketemon / pyuplift
  Lightweight uplift modeling framework for Python
  ☆28Updated 5 years ago
• usaito / counterfactual-cv
  (ICML2020) “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models’’
  ☆31Updated 2 years ago
• soerenkuenzel / causalToolbox
  ☆42Updated 3 years ago
• nlapier2 / PySensemakr
  Python implementation of the original R sensemakr package: https://github.com/carloscinelli/sensemakr
  ☆49Updated 3 months ago
• AliciaCurth / CATENets
  Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
  ☆136Updated 9 months ago