A Conversation on Causality at Our Table

Brewing coffee, exchanging words. As the steam drifts upward, time softens, and the world of statistics opens little by little.

These small conversations between a statistician father and his clinician daughter became the seed of this site. Questions born in the middle of research, numbers that snagged while reading a paper, and occasionally, glimpses of how researchers carry themselves—the things that truly matter tend to be scattered quietly across multiple layers: everyday life, clinical practice, causality, data, cognition, and language.

Over coffee, their light conversation gathers these fragments one by one, as if weaving them into a single story without disturbing their totality.

From survival curves and misread p-values to the layered relationship between causality and statistical inference— the episodes hide small clues like a gentle spiral, yet speak in words warm enough to hold between your hands on a winter morning.

Mathematics and medicine make their appearances, but feel free to take only what you need. Every discussion can be reproduced in R. And as their dialogue unfolds, so too does a subtle shift in how the world appears.

Enjoy following that changing view, at the quiet pace of a cup of coffee.

Episodes

1. Study Design — Where research begins

Every study starts with a question. How do we frame PICO/PECO? How do we choose outcomes? How do we protect against bias? The daughter asks her father about the very starting point of clinical research.

You can download the full R script for this episode here. If you are curious about the R packages and concrete analysis steps, feel free to take a look.

2. Glossary — The words of statistics

  • [Statistical Terms in Plain Language]

3. Frequentist Thinking — Practicing statistics

Using cancer clinical trials as an example, my father explains the frequentist perspective of “How do numbers behave when you virtually repeat a study in a long run?”

  • [Reading a Paper over a Cup of Coffee]
  • [P-Value Explanations That Seem Plausible at First Glance]
  • [Beyond 0.05: Interpreting P-Values in a Clinical Trial]

You can download the full R script for this and the next episodes here.

4. Frequentist Experiments — Validation of statistics

The laws derived from the frequentist theory are not mere abstractions; they can be verified through simulation experiments. Even if you are unfamiliar with R, simply run the script to reproduce the results.

  • [R Demonstration of Bias in Kaplan-Meier Under Competing Risks]
  • [Understanding Confidence Intervals via Hypothetical Replications in R]
  • [Alpha, Beta, and Power: The Fundamental Probabilities Behind Sample Size]

You can download the full R script for this and the previous episode here.

5. Effects and Time — How effects evolve

We unravel risk differences, risk ratios, hazard ratios, vaccine effectiveness rates, attributable fractions, and effects that change over time alongside survival curves and cumulative incidence curves. A multidimensional perspective on data quietly develops, paving the way for causal inference.

  • [Silent Confusions Hidden in Percentages]
  • [Who Is This Percentage About? Target Populations and Attributable Fractions]
  • [Understanding Collapsibility of Effect Measures in Marginal and Stratified Analyses with R]
  • [When Odds Ratios Approximate Risk Ratios—and When They Fail]
  • [From Risk and Rate to Survival and Hazard]
  • [Distinguishing Time-Point, Time-Constant, and Time-Varying Effects: An R Example]

You can download the full R script for this and the previous episode here.

6. Adjusting for Bias — The landscape of regression modeling

An episode where she learns from her father — from interpreting logistic regression results and creating tables to avoiding pitfalls in its use. And then…

  • [From Risk to Logistic Regression]
  • [Logit: How a Transformation Shapes an Effect]
  • [Where My Logistic Regression Went Wrong]
  • [Why Logistic Regression Fails in Small Samples]
  • [Simpson’s paradox]

7. Truth — What defines it?

  • [What Data Cannot Tell Us]
  • [What Could Have Happened]
  • [What Is It That You Want to Know?]

8. Causal Inference — To find causality

Directed Acyclic Graphs (DAGs), common causes, colliders, mediators, backdoor criteria. We try to talk about the language of causality needed to think about “what if?”, incorporating the minimal necessary mathematical expressions concerning probability.

  • [Three-Variable DAGs: The Smallest Building Blocks of Causal Structure]
  • [A Subtle Distinction between Common Causes and Confounders]
  • [DAGs and Conditional Distributions: Two Languages for the Same Structure]
  • [A Circle, an Equation, and a Cylinder]
  • [Backdoor Paths, Block, and d-Separation: A Clue for Adjusting for Bias]

9. Publish a Paper — Beyond the research

How to communicate research findings, creating charts and graphs, tips for revising, navigating peer review. A quiet lecture for researchers, exploring what lies beyond statistics.

  • [A Subtle Distinction Between Editors and Reviewers]
  • [Three Tips for Writing a Paper]
  • [Communicating with Care]
  • [A Morning Just Before Submission]

How to read this site

First, reading the Study Design series will give you an overview of the clinical research landscape and outline the process of “what questions to ask and what data to collect.” From there, you can either follow the narrative or choose the layer that interests you most.

  • Want to build a foundation for reading papers: Frequentist Thinking
  • Want to understand the meaning of numbers and the practice of statistical analysis more deeply: Effects and Time / Adjusting for bias
  • Want to organize the invisible structure of causality: Causal Inference
  • Want to see the process through to shaping and delivering your research: Publish a Paper

While each episode stands independently, reading the whole reveals how everyday life, clinical practice, causality, data, cognition, and language connect as one flow. Gradually, a map of the research endeavor sketches itself in your mind. Grab your favorite drink and enjoy the journey of conversation between the two.

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