jrfiedler/causal_inference_python_code
Causal Inference Python Code
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.
