Alternatives and similar repositories for cem:

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

• microsoft / SparseSC
  Fit Sparse Synthetic Control Models in Python
  ☆81Updated last year
• nlapier2 / PySensemakr
  Python implementation of the original R sensemakr package: https://github.com/carloscinelli/sensemakr
  ☆51Updated 4 months ago
• grf-labs / policytree
  Policy learning via doubly robust empirical welfare maximization over trees
  ☆78Updated 10 months ago
• bernardodionisi / differences
  difference-in-differences in Python
  ☆100Updated last year
• NickCH-K / causaldata
  Packages of Example Data for The Effect
  ☆138Updated 5 months ago
• MasaAsami / pysynthdid
  Synthetic difference in differences for Python
  ☆76Updated last year
• DoubleML / doubleml-docs
  Documentation and User Guide for DoubleML - Double Machine Learning in Python & R
  ☆21Updated this week
• IQSS / cem
  ☆17Updated 2 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 2 years ago
• vsemenova / orthoml
  Code associated with paper: Orthogonal Machine Learning for Demand Estimation: High-Dimensional Causal Inference in Dynamic Panels, Seme…
  ☆25Updated last year
• sdfordham / pysyncon
  A python module for the synthetic control method
  ☆64Updated last week
• soerenkuenzel / causalToolbox
  ☆42Updated 3 years ago
• grf-labs / sufrep
  Sufficient Representation for Categorical Variables https://arxiv.org/abs/1908.09874v1
  ☆14Updated 5 years ago
• syrgkanislab / orthogonal_regularized_estimation
  Code associated with paper: Plug-in Regularized Estimation of High-Dimensional Parameters in Nonlinear Semiparametric Models, Chernozhuk…
  ☆16Updated 4 years ago
• zalandoresearch / ACIC23-competition
  Data for and description of the ACIC 2023 data competition
  ☆32Updated last year
• socket778 / XBCF
  R and python implementations of Accelerated Bayesian Causal Forest.
  ☆25Updated 9 months ago
• edgeslab / CTL
  A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python
  ☆62Updated last year
• timmens / causal-forest
  Implements the Causal Forest algorithm formulated in Athey and Wager (2018).
  ☆71Updated 4 years ago
• nppackages / scpi
  Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.
  ☆33Updated 2 months ago
• OscarEngelbrektson / SyntheticControlMethods
  A Python package for causal inference using Synthetic Controls
  ☆181Updated last year
• ehkennedy / npcausal
  ☆94Updated last year
• almost-matching-exactly / DAME-FLAME-Python-Package
  A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data
  ☆56Updated 10 months ago
• victor5as / RieszLearning
  Replication files for Chernozhukov, Newey, Quintas-Martínez and Syrgkanis (2021) "RieszNet and ForestRiesz: Automatic Debiased Machine Le…
  ☆14Updated 2 years ago
• xnie / rlearner
  Quasi-Oracle Estimation of Heterogeneous Treatment Effects
  ☆108Updated 4 years ago
• vdorie / bartCause
  Causal Inference using Bayesian Additive Regression Trees
  ☆79Updated 7 months ago
• MCKnaus / dmlmt
  Double Machine Learning for Multiple Treatments
  ☆40Updated 4 years 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
• nredell / shapFlex
  An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
  ☆74Updated 4 years ago
• hmelberg / causal
  notebooks on methods for causal inference
  ☆32Updated 8 months ago
• annennenne / causalDisco
  Tools for causal discovery in R
  ☆19Updated last month