Alternatives and similar repositories for shopper-src:

Users that are interested in shopper-src are comparing it to the libraries listed below

• JingyuHe / XBART
  ☆92Updated 3 months ago
• FlorianWilhelm / bhm-at-scale
  🪜 Bayesian Hierarchical Models at Scale
  ☆52Updated 3 years ago
• Nth-iteration-labs / contextual
  Contextual Bandits in R - simulation and evaluation of Multi-Armed Bandit Policies
  ☆79Updated 4 years ago
• grf-labs / policytree
  Policy learning via doubly robust empirical welfare maximization over trees
  ☆78Updated 9 months ago
• swager / causalForest
  DEPRECATED. See new generalized random forest package for up-to-date implementation.
  ☆52Updated 7 years ago
• 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
• crflynn / skgrf
  scikit-learn compatible Python bindings for grf (generalized random forests) C++ random forest library
  ☆31Updated 2 years ago
• microsoft / SparseSC
  Fit Sparse Synthetic Control Models in Python
  ☆80Updated 11 months ago
• gsbDBI / ds-wgan
  Design of Simulations using WGAN
  ☆49Updated 2 years ago
• ehkennedy / npcausal
  ☆93Updated last year
• bernardodionisi / differences
  difference-in-differences in Python
  ☆100Updated last year
• jhartford / DeepIV
  Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction
  ☆156Updated 3 years ago
• vdorie / npci
  Non-parametrics for Causal Inference
  ☆43Updated 3 years ago
• MasaAsami / pysynthdid
  Synthetic difference in differences for Python
  ☆76Updated 11 months ago
• edgeslab / CTL
  A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python
  ☆62Updated last year
• zalandoresearch / ACIC23-competition
  Data for and description of the ACIC 2023 data competition
  ☆32Updated last year
• soerenkuenzel / causalToolbox
  ☆42Updated 3 years ago
• vsemenova / orthoml
  Code associated with paper: Orthogonal Machine Learning for Demand Estimation: High-Dimensional Causal Inference in Dynamic Panels, Seme…
  ☆24Updated last year
• mattiasvillani / AdvBayesLearnCourse
  Course material for the PhD course in Advanced Bayesian Learning
  ☆59Updated 3 weeks ago
• gostevehoward / confseq
  Confidence sequences and uniform boundaries
  ☆61Updated 4 months ago
• nlapier2 / PySensemakr
  Python implementation of the original R sensemakr package: https://github.com/carloscinelli/sensemakr
  ☆49Updated 3 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
• larsroemheld / causalinf_ex_elasticity
  A full example for causal inference on real-world retail data, for elasticity estimation
  ☆50Updated 3 years ago
• timmens / causal-forest
  Implements the Causal Forest algorithm formulated in Athey and Wager (2018).
  ☆69Updated 4 years ago
• syrgkanislab / orthogonal_regularized_estimation
  Code associated with paper: Plug-in Regularized Estimation of High-Dimensional Parameters in Nonlinear Semiparametric Models, Chernozhuk…
  ☆15Updated 4 years ago
• jeffalstott / pystan_time_series
  Basic time series modeling with Stan and Pystan
  ☆33Updated 7 years ago
• pierrejacob / unbiasedmcmc
  Unbiased Markov chain Monte Carlo with couplings
  ☆29Updated 2 years ago
• socket778 / XBCF
  R and python implementations of Accelerated Bayesian Causal Forest.
  ☆25Updated 8 months 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
• ryantibs / conformal
  Tools for conformal inference in regression
  ☆236Updated 7 months ago