Alternatives and similar repositories for orthogonal_regularized_estimation

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

• inovex / justcause
  💊 Comparing causality methods in a fair and just way.
  ☆140Updated 5 years ago
• yaronshap / GloballyConsistentRules
  Implementation of algorithms from the paper "Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application…
  ☆25Updated 3 years ago
• usaito / counterfactual-cv
  (ICML2020) “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models’’
  ☆31Updated 2 years ago
• IliasZadik / double_orthogonal_ml
  ☆11Updated 7 years ago
• 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
  ☆62Updated last month
• vsemenova / orthoml
  Code associated with paper: Orthogonal Machine Learning for Demand Estimation: High-Dimensional Causal Inference in Dynamic Panels, Seme…
  ☆25Updated 2 years 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
• max-little / GRAPL
  GRAPL: A computational library for nonparametric structural causal modelling, analysis and inference
  ☆83Updated 8 months ago
• gsbDBI / ds-wgan
  Design of Simulations using WGAN
  ☆53Updated 2 years ago
• dandls / moc
  Multi-Objective Counterfactuals
  ☆42Updated 3 years ago
• zalandoresearch / ACIC23-competition
  Data for and description of the ACIC 2023 data competition
  ☆32Updated 2 years ago
• andrewtavis / causeinfer
  Machine learning based causal inference/uplift in Python
  ☆61Updated last year
• FlorianWilhelm / bhm-at-scale
  🪜 Bayesian Hierarchical Models at Scale
  ☆51Updated 4 years ago
• rtm2130 / MST
  Package for building Market Segmentation Trees, Choice Model Trees, and Isotonic Regression Trees
  ☆17Updated 2 years ago
• 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
• nlapier2 / PySensemakr
  Python implementation of the original R sensemakr package: https://github.com/carloscinelli/sensemakr
  ☆52Updated 4 months ago
• deshen24 / syntheticNN
  ☆24Updated 3 years ago
• FMenchetti / CausalArima
  An R package to estimate the effect of interventions on univariate time series using ARIMA models
  ☆24Updated last year
• AlxndrMlk / blogs-code
  Code for blog posts.
  ☆20Updated last year
• almost-matching-exactly / MALTS
  ☆16Updated last year
• Ibotta / mr_uplift
  Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and mu…
  ☆70Updated 4 months ago
• CausalML / interventions-disparate-impact-responders
  Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds
  ☆12Updated 5 years ago
• causal-machine-learning / kdd2021-tutorial
  EconML/CausalML KDD 2021 Tutorial
  ☆162Updated 2 years ago
• matthewvowels1 / Awesome-Causal-Inference
  A curated list of awesome work on causal inference, particularly in machine learning.
  ☆108Updated 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 4 months ago
• vdorie / npci
  Non-parametrics for Causal Inference
  ☆49Updated 3 years ago
• microsoft / SparseSC
  Fit Sparse Synthetic Control Models in Python
  ☆86Updated last year
• tomlinsonk / choice-set-opt
  Code accompanying our ICML 2020 paper on choice set optimization in group decision-making.
  ☆11Updated 5 years ago
• bernardodionisi / differences
  difference-in-differences in Python
  ☆105Updated last year
• VowpalWabbit / estimators
  Estimators to perform off-policy evaluation
  ☆13Updated last year