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.
This project analyses whether people who have attained job trainings makes more money than those who have not attained these trainings.
CCNETS is uniquely crafted to emulate brain-like information processing and comprises three main components: explainer, producer, and reasoner. Each component is designed to mimic specific brain functions, which aids in generating high-quality datasets and enhancing the classification performance
A python package providing Contains a few functions useful for data-analysis, causal inference etc (built mostly for personal use)
Code and data for the paper, "Automatically Neutralizing Subjective Bias in Text"
A large pile of interesting and/or useful information
This is the repository of the project Causality Extraction in Business Articles for the Applied Natural Language Processing Class.
Delusions in sequence models for interaction and control
A novel method of score-based causal discovery using an adversarially trained neural causal model (NCM)
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"
Learning high-dimensional causal effect with deep models
ArCO-GP, an effective method for Bayesian causal inference via structural marginalisation and neural autoregressive orders.
[NeurIPS 2020] Causal Discovery in Physical Systems from Videos
This project leverages data science skills, including least squares regression, causal inference techniques, and decision tree classification in order to answer to distinct questions regarding Democratic Candidate primary election results.
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