Alternatives and similar repositories for deconfounder_public:

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

• blei-lab / deconfounder_tutorial
  ☆87Updated 4 years ago
• vveitch / causality-tutorials
  Short tutorials on the use of machine learning methods for causal inference
  ☆49Updated 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
  ☆81Updated 6 years ago
• bradyneal / realcause
  Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…
  ☆71Updated 3 years ago
• vdorie / npci
  Non-parametrics for Causal Inference
  ☆43Updated 2 years ago
• vveitch / causal-network-embeddings
  Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"
  ☆55Updated last year
• usaito / counterfactual-cv
  (ICML2020) “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models’’
  ☆31Updated last year
• jhartford / DeepIV
  Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction
  ☆155Updated 3 years ago
• AliciaCurth / CATENets
  Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
  ☆133Updated 7 months ago
• ioanabica / Counterfactual-Recurrent-Network
  Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…
  ☆58Updated 10 months ago
• charmlab / recourse
  Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
  ☆36Updated 2 years ago
• causalens / cdml-neurips2020
  This repository captures source code and data sets for our paper at the Causal Discovery & Causality-Inspired Machine Learning Workshop a…
  ☆59Updated 5 months ago
• franrruiz / shopper-src
  Code for Shopper, a probabilistic model of shopping baskets
  ☆52Updated 4 years ago
• matthewvowels1 / Awesome-Causal-Inference
  A curated list of awesome work on causal inference, particularly in machine learning.
  ☆98Updated 3 years ago
• deshen24 / syntheticNN
  ☆22Updated 3 years ago
• bradyneal / causal-book-code
  ☆76Updated 4 years ago
• ijmbarr / notes-on-causal-inference
  Some notes on Causal Inference, with examples in python
  ☆149Updated 4 years ago
• d909b / drnet
  💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatm…
  ☆86Updated last year
• CausalML / DeepGMM
  ☆33Updated 5 years ago
• gpleiss / equalized_odds_and_calibration
  Code and data for the experiments in "On Fairness and Calibration"
  ☆50Updated 2 years ago
• msesia / conditional-conformal-pvalues
  Conditional calibration of conformal p-values for outlier detection.
  ☆34Updated 2 years ago
• klasnja / HeartStepsV1
  Data from the HeartSteps V1 study
  ☆12Updated 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 8 months ago
• d909b / perfect_match
  ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.
  ☆125Updated last year
• ElementAI / causal_discovery_toolbox
  Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
  ☆30Updated 5 years ago
• JonathanCrabbe / Symbolic-Pursuit
  Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"
  ☆15Updated 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".
  ☆27Updated 3 years ago
• CausalML / interventions-disparate-impact-responders
  Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds
  ☆11Updated 5 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
• max-little / GRAPL
  GRAPL: A computational library for nonparametric structural causal modelling, analysis and inference
  ☆79Updated last month