An article explaining the concepts of unconditional independence, unconditional dependence, conditional independence, and conditional dependence in causal inference through simple examples.
ASCVIT V1 aims to make data analysis easier by automating statistical calculations, visualizations, and interpretations.
Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.
- Accepts CSV or Excel files. Provides a data overview including summary statistics, variable types, and data points.
- Histograms, boxplots, pairplots, correlation matrices.
- t-tests, ANOVA, chi-square test.
- Linear, logistic, and multivariate regression.
- Time series analysis.
- k-means, hierarchical clustering, DBSCAN.
Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
This article demonstrates how basic statistics and techniques like PCA can be used to analyze tabular datasets, highlighting the importance of data preprocessing, statistical tests, and handling multicollinearity.
A Python package for the statistical analysis of A/B tests featuring Student's t-test, Z-test, Bootstrap, and quantile metrics out of the box.
Explores the role of conditional probability in understanding events and Bayes' theorem, with examples in regression analysis and everyday scenarios, demonstrating how our biological tissue runs probabilistic machinery.
This article discusses the differences between predictive and causal inference, explains why correlation does not imply causation, and why machine learning is not inherently suited for causal inference. It highlights the limitations of using machine learning for causal estimation and provides suggestions for when each type of inference should be used. The article also touches on causal machine learning and its role in addressing the challenges of high-dimensional data and complex functional forms.
The article explains how to apply Friedman's h-statistic to understand if complex machine learning models use interactions to make predictions. It uses the artemis package and interprets the pairwise, overall, and unnormalised metrics.
This article explains the PCA algorithm and its implementation in Python. It covers key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues. The tutorial aims to provide a solid understanding of the algorithm's inner workings and its application for dealing with high-dimensional data and the curse of dimensionality.