Machine Learning With Statistical and Causal Methods in R for Data Science

This article explains how to integrate statistical modeling, predictive machine learning, and causal inference in R to build data science workflows that are not only accurate but also interpretable and capable of uncovering true cause-and-effect relationships.

Article Outline

1. Introduction

   • Why both statistical and causal methods matter in modern data science.

   • The difference between predictive power and causal understanding.

   • Real-world contexts where causality is essential (policy evaluation, treatment effects, finance, healthcare).

2. Statistical Foundations of Machine Learning

   • How regression-based approaches (linear/logistic regression) provide interpretable relationships between features and outcomes.

   • Statistical inference: coefficients, confidence intervals, and hypothesis testing.

   • Role of assumptions and transparency in statistical modeling.

3. Causal Inference in Machine Learning

   • Distinguishing correlation from causation.

   • Key frameworks: Rubin’s Potential Outcomes Model, Directed Acyclic Graphs (DAGs), counterfactual reasoning.

   • Applications of causal inference for answering what-if questions.

4. Integrating Statistical and Causal Approaches

   • When to use statistical models for inference, machine learning models for prediction, and causal inference for decision-making.

   • Complementary roles: predictive accuracy, interpretability, and causal insight.

   • Examples of combining logistic regression, random forests, and causal models.

5. R Environment Setup

   • Core packages:

     • tidyverse for data manipulation and visualization.

     • caret for machine learning models.

     • stats and glm for statistical regression.

     • MatchIt or causalTree for causal inference.

   • Why R is well-suited for combining statistical rigor with modern machine learning.

6. Data Preparation

   • Simulating a dataset with predictors, treatment, and outcome variables.

   • Introducing confounders to show the need for causal adjustment.

   • Splitting data into training and testing sets for predictive evaluation.

7. Statistical Modeling Example

   • Using logistic regression to estimate the relationship between features and outcome.

   • Interpreting coefficients, odds ratios, and statistical significance.

   • Discussing limitations when interpreting coefficients as causal effects.

8. Machine Learning Model Example

   • Training a Random Forest model for predictive accuracy.

   • Comparing performance metrics such as accuracy and AUC against logistic regression.

   • Highlighting the trade-off between accuracy and interpretability.

9. Causal Inference Example

   • Estimating the Average Treatment Effect (ATE) with matching methods.

   • Adjusting for confounders and comparing naive estimates to causal estimates.

   • Showing how treatment effect estimation provides actionable insights beyond prediction.

10. End-to-End Example in R

    • Full workflow: dataset creation → statistical regression → machine learning classification → causal analysis.

    • Code demonstrations for each step, with clear explanations.

    • Visualisations of predictive performance (ROC curves) and causal effect estimates.

11. Applications in Data Science

    • Finance: evaluating the effect of subsidies on loan repayment.

    • Healthcare: assessing treatment effectiveness while controlling for confounders.

    • Marketing: measuring causal impact of campaigns beyond predictive click-through models.

12. Common Pitfalls and Best Practices

    • Misinterpreting predictive associations as causal effects.

    • Overfitting machine learning models without generalizable inference.

    • Ignoring confounders in causal analysis.

    • Best practices for reporting both predictive and causal results.

13. Conclusion and Future Directions

    • Recap of how statistical, predictive, and causal methods complement one another.

    • Importance of integrating all three for robust, interpretable, and actionable insights.

    • Emerging directions: causal forests, Bayesian causal inference, and causal deep learning.

Introduction

Machine learning has transformed the landscape of data science by providing powerful predictive models capable of uncovering complex patterns in data. However, predictive accuracy alone does not suffice for many real-world applications where decision-making requires an understanding of underlying relationships and interventions. In areas like finance, healthcare, and policy evaluation, data scientists must answer not only what is likely to happen? but also what would happen if we intervened?. Statistical methods provide interpretability and inference, while causal methods allow for the identification of cause-and-effect relationships. This article provides an in-depth exploration of combining statistical, machine learning, and causal approaches in R with a complete reproducible example using simulated data.

We will structure this article into a journey beginning with the fundamentals of statistical modeling, moving through machine learning methods for prediction, and concluding with causal inference to uncover treatment effects. By the end, readers will understand how to integrate these complementary perspectives to create workflows that are accurate, interpretable, and causally robust.

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Statistical Foundations of Machine Learning

Statistical methods form the foundation of many machine learning algorithms. They offer insights into relationships between features and outcomes, often expressed through parameters and assumptions. A common example is logistic regression, which models the probability of a binary outcome as a function of predictor variables.

For instance, in financial analysis, logistic regression can assess how income, employment status, and credit history relate to loan default probability. The resulting coefficients provide interpretable effect sizes, while standard errors and p-values quantify uncertainty.

The strengths of statistical approaches include:

• Interpretability of coefficients.

• Ability to test hypotheses.

• Explicit assumptions that clarify the model’s scope and limitations.

These characteristics make statistical models invaluable when we need more than predictive accuracy and require insights into how variables influence outcomes.

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