What are the main features of jrfiedler/causal_inference_python_code?

The main features of jrfiedler/causal_inference_python_code are: Causal Effect Estimators, Causal Inference, G-Formula Estimators, G-Formula Standardizers, Regression Analysis, Causal Counterfactual Simulators, Data Science, Observational Data Analysis Tools.

What are some open-source alternatives to jrfiedler/causal_inference_python_code?

Open-source alternatives to jrfiedler/causal_inference_python_code include: py-why/dowhy — DoWhy is an open-source Python library for causal inference that structures the entire analysis into a sequential… py-why/econml — EconML is a Python library for causal inference designed to estimate heterogeneous treatment effects using a… uber/causalml — CausalML is a machine learning library for causal inference, providing tools to estimate treatment effects and causal… rmcelreath/stat_rethinking_2022 — This project is a collection of Bayesian statistics courseware and educational resources. It provides instructional… statsmodels/statsmodels — Statsmodels is a comprehensive Python library designed for statistical modeling, econometric research, and data… camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers — This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for…

Causal Inference Python Code - estimer les ef…

Ce dépôt propose une collection d'implémentations Python pour l'inférence causale, conçues pour estimer l'impact d'interventions spécifiques à partir de données observationnelles. Il sert de boîte à outils statistique permettant aux chercheurs d'isoler les signaux de causalité des facteurs de confusion complexes dans des jeux de données dépourvus de contrôle expérimental.

Le framework permet d'appliquer des méthodologies rigoureuses pour étudier les déterminants de santé et évaluer les interventions politiques. En utilisant la modélisation causale structurelle et les graphes acycliques dirigés, la bibliothèque permet aux utilisateurs de cartographier les dépendances causales et d'identifier les variables nécessaires à une estimation non biaisée. Elle prend en charge la simulation de résultats contrefactuels pour comparer des résultats potentiels selon différents scénarios de traitement, offrant une approche structurée pour comprendre les relations de cause à effet.

La boîte à outils couvre un large éventail de techniques d'estimation statistique, notamment la pondération par l'inverse de la probabilité, le calcul par g-formule et l'analyse de régression paramétrique. Ces outils de calcul sont organisés pour faciliter l'analyse de données observationnelles dans des contextes de recherche épidémiologique et sociale. Le projet est distribué sous forme de collection de Jupyter Notebooks contenant ces frameworks et implémentations statistiques.

Features

Causal Effect Estimators - Calculates the impact of specific interventions on outcomes by applying statistical methodologies to observational data sets.
Causal Inference - Provides a collection of statistical methods for estimating causal effects from observational data using techniques like inverse probability weighting and g-formula.
G-Formula Estimators - Estimates causal effects by standardizing the distribution of outcomes across treatment groups using conditional probability models.
G-Formula Standardizers - Estimates causal effects by integrating conditional probability models to simulate outcomes across different hypothetical treatment distributions.
Regression Analysis - Uses linear or generalized models to quantify the relationship between treatment variables and outcomes while controlling for covariates.
Causal Counterfactual Simulators - Simulates potential outcomes under different treatment scenarios to estimate the difference between observed and hypothetical states.
Data Science - Implements computational techniques to identify and quantify cause and effect relationships within data sets lacking experimental control.
Observational Data Analysis Tools - Analyzes data collected without experimental control to identify potential causal relationships between variables using structured statistical frameworks.
Observational Data Processors - Applies rigorous statistical frameworks to non-experimental data sets to isolate causal signals from complex confounding factors.
Epidemiological Research Methodologies - Applies rigorous causal inference methodologies to study determinants of health and evaluate the impact of policy interventions.
Epidemiological Analysis Frameworks - Uses structured statistical frameworks to study health determinants and evaluate the potential effects of public health policy interventions.
Epidemiological Research Methods - Applies advanced statistical techniques to study the distribution and determinants of health-related states or events in specified populations.
Inverse Probability Weighting Estimators - Adjusts for selection bias by weighting observations based on the inverse of their predicted probability of receiving a treatment.
Statistical Estimation - Fits generalized linear models to observational data to quantify the relationship between interventions and observed outcomes.
Causal Effect Estimators - Calculates causal parameters by fitting mathematical models to observational data to isolate the effect of specific interventions.
Public Health Policy Evaluators - Assesses the potential outcomes of policy interventions by modeling causal pathways to inform evidence-based decision making.
Research and Data Analysis Tools - Facilitates the modeling of counterfactual outcomes and the evaluation of intervention impacts through structured research workflows.
Statistical Analysis Libraries - Offers computational tools for calculating the impact of interventions on outcomes within complex research data sets.
Directed Acyclic Graph Models - Maps causal dependencies using visual diagrams to identify necessary variables for unbiased statistical estimation.

py-why/dowhy

8,175Voir sur GitHub

DoWhy is an open-source Python library for causal inference that structures the entire analysis into a sequential four-step framework: modeling, identification, estimation, and refutation. It treats causal assumptions as explicit, first-class citizens, represented as directed acyclic graphs that can be automatically validated against observed data. The library distinguishes itself by cleanly separating the causal identification problem from statistical estimation, allowing any compatible estimator to be used for a given target estimand. It includes automated refutation testing that validates

py-why/EconML

4,683Voir sur GitHub

EconML is a Python library for causal inference designed to estimate heterogeneous treatment effects using a combination of machine learning and econometrics. It serves as a toolkit for calculating conditional average treatment effects to determine how specific interventions impact individuals or subgroups. The project provides a framework for double machine learning and orthogonal machine learning to isolate causal signals from high-dimensional confounders. It includes specialized implementations for causal forests and instrumental variable learners, allowing for the recovery of causal relat

uber/causalml

5,875Voir sur GitHub

CausalML is a machine learning library for causal inference, providing tools to estimate treatment effects and causal impacts using experimental and observational data. It functions as a framework for uplift modeling and the estimation of heterogeneous treatment effects to distinguish causation from correlation. The library focuses on identifying how different user segments respond to specific interventions. This includes calculating the incremental gain of target metrics to optimize marketing campaigns, targeting high-response customer segments, and personalizing user engagement through the

rmcelreath/stat_rethinking_2022

4,103Voir sur GitHub

This project is a collection of Bayesian statistics courseware and educational resources. It provides instructional materials, problem sets, and solutions designed for learning Bayesian data analysis and causal modeling. The repository includes a suite of statistical data visualization scripts used to generate instructional animations and plots. It also contains code examples that implement Bayesian modeling and survival analysis across multiple programming languages to demonstrate different computational approaches. The materials cover a range of statistical capabilities, including causal i

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jrfiedlercausal_inference_python_code

Causal Inference Python Code

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