Pytorch implementation of "Adapting Text Embeddings for Causal Inference"
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Updated
Sep 4, 2020 - Python
Pytorch implementation of "Adapting Text Embeddings for Causal Inference"
Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
CFAD: Achieving Counterfactual Fairness for Anomaly Detection (PAKDD 2023)
A PyTorch implementation of the "robust" synthetic control model
Independent Component Analysis in Linear Time-Invariant Systems
EMNLP'22, CEM improves MHCH performance by correcting prediction bias and training an auxiliary cost simulator based on user state and labor cost causal graph, without requiring complex model crafting.
Causal machine learning course in Python
This project analyses whether people who have attained job trainings makes more money than those who have not attained these trainings.
Python package for CITS algorithm: Causal inference from time series data
Code for the research paper Meta-learning with hierarchical models based on similarity of causal mechanisms
nl-causal: nonlinear causal inference based on IV regression in Python
implement machine learning models from scratch
minimal, fast object oriented implementation of the AIPW estimator for many discrete treatments. Implemented with scikitlearners and cross-fitting. Written primarily for OOP practice and customisation.
CausalLift: Python package for causality-based Uplift Modeling in real-world business
Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"
Python implementation of CDCI, a method to identify causal direction between two variables
Code for the paper "Hidden yet quantifiable: A lower bound for confounding strength using randomized trials"
Implementation of neural network algorithm for estimation of heterogeneous treatment effects and propensity scores described in Farrell, Liang, and Misra (2021)
sample code for causal discovery by Lingam (with hidden variable).
Conditional Divergence based Causal Inference (CDCI) - CLeaR 2022
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