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causal-inference
Here are 401 public repositories matching this topic...
Coz: Causal Profiling
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May 24, 2021 - C
Uplift modeling and causal inference with machine learning algorithms
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Jul 25, 2021 - Python
Python Library for learning (Structure and Parameter) and inference (Probabilistic and Causal) in Bayesian Networks.
python
statistics
statistical-inference
bayesian-networks
probabilistic-graphical-models
causal-inference
structure-learning
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Jul 25, 2021 - Python
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
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Jul 23, 2021 - Jupyter Notebook
An index of algorithms for learning causality with data
awesome
learning-to-rank
recommender-system
causality
causality-analysis
causal-inference
multilabel-classification
baselines
causality-algorithms
unconfoundedness-assumption
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Jun 24, 2021
A Python library that helps data scientists to infer causation rather than observing correlation.
data-science
machine-learning
bayesian-inference
bayesian-networks
causal-inference
causal-models
causal-networks
causalnex
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Jun 19, 2021 - Python
Resources to learn more about Machine Learning and Artificial Intelligence
machine-learning
natural-language-processing
information-retrieval
reinforcement-learning
deep-learning
artificial-intelligence
knowledge-graph
question-answering
probabilistic-programming
bayesian-inference
recommender-systems
causal-inference
knowledge-representation
reasoning
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May 27, 2021
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.
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Jul 25, 2021 - Jupyter Notebook
tibshirani
commented
Sep 5, 2018
I ran a regression_forest for > 10 minutes and had no idea if it would complete in 15 min or an hour.
It would be great to have an argument "verbose" (default FALSE) which causes the function to
print the function's progress, to help the user estimate the remaining time before completion.
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
python
machine-learning
algorithm
graph
inference
toolbox
causality
causal-inference
causal-models
graph-structure-recovery
causal-discovery
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Jul 16, 2021 - Python
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Jan 12, 2021 - R
Open-source Python library for statistical analysis of randomised control trials (A/B tests)
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Mar 31, 2021 - Python
maks-sh
commented
Jul 20, 2021
Statistical Rethinking (2nd ed.) with NumPyro
python
numpy
variational-inference
causal-inference
bayesian-statistics
markov-chain-monte-carlo
laplace-approximation
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Jul 24, 2021 - Jupyter Notebook
Code for the Recsys 2018 paper entitled Causal Embeddings for Recommandation.
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Oct 24, 2018 - Python
CausalLift: Python package for causality-based Uplift Modeling in real-world business
econometrics
causality
propensity-scores
causal-inference
uplift-modeling
counterfactual
causal-impact
propensity-score
uplift
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Mar 5, 2021 - Python
We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.
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Jul 15, 2021
Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
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May 21, 2021 - Python
Repository for Deep Structural Causal Models for Tractable Counterfactual Inference
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Mar 5, 2021 - Jupyter Notebook
A (concise) curated list of awesome Causal Inference resources.
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Nov 17, 2020
Must-read papers and resources related to causal inference and machine (deep) learning
representation-learning
causal-inference
treatment-effects
causal-models
counterfactual
randomized-controlled-trials
paper-list
heterogeneous-treatment-effects
causal-discovery
counterfactual-learning
estimating-treatment-effects
causal-learning
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Jun 28, 2021
Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction
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Jun 17, 2021 - Python
Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3
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Dec 30, 2020 - Jupyter Notebook
A selection of state-of-the-art research materials on decision making and motion planning.
machine-learning
reinforcement-learning
deep-learning
algorithms
robotics
decision-making
motion-planning
artificial-intelligence
trajectory-generation
autonomous-vehicles
causal-inference
inverse-reinforcement-learning
intelligent-transportation-systems
trajectory-prediction
motion-control
multiagent-reinforcement-learning
multi-agent-learning
trajectory-planning
motion-prediction
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Aug 26, 2020
This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations.
conversations
emotion
inference
dataset
causality
natural-language-inference
causal-inference
dialogue-systems
reasoning
emotion-recognition
causal-models
dialogue-generation
roberta
bert-model
emotion-recognition-in-conversation
emotion-cause
emotion-cause-pair-extraction
emotion-tasks
causal-spans
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Jul 24, 2021 - Python
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May 21, 2021 - Python
Python package for the creation, manipulation, and learning of Causal DAGs
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Jul 13, 2021 - JavaScript
Causal inference, graphical models and structure learning with the PC algorithm.
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Jun 29, 2021 - Julia
Awesome Neural Logic and Causality: MLN, NLRL, NLM, etc. 因果推断，神经逻辑，强人工智能逻辑推理前沿领域。
neural-network
logic
first-order-logic
markov
logic-programming
causality
causal-inference
causal
inductive-logic-programming
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Oct 13, 2020
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When you miss declaring a node in your causal graph, it's going to throw a
KeyError: 'label'error. It could be more explicit to make debugging easier. I think it would be nice to inform what is the node hough used in the graph.