Wellbeing Insights

In traditional thinking, an observational study (where researchers simply collect data without intervention) is often seen as inferior to an experiment or RCT (where the researcher actively intervenes, e.g. assigns treatment randomly).

The modern causal perspective challenges this hierarchy. Observational and experimental data aren't a dichotomy (one "bad" and one "good"), but two points on a continuum of causal inquiry. The key difference is not in the data itself, but in how we interpret it and what we know about the data-generating process.

The Two Ends of the Spectrum

Experiments explicitly manipulate causes:

In an RCT, we physically do something (e.g. change room temperature) and observe the effect. Randomization is powerful because it deliberately breaks any pre-existing links between the treatment and other factors, mimicking a clean intervention in a causal model.

Observational studies watch causes and effects in the wild:

Here, researchers just observe what happens naturally, without random assignment. This data is subject to biases – maybe people who naturally prefer cooler rooms also have other sleep-promoting habits. However, if we have enough knowledge to adjust for those biases, observational data can answer the same causal question.

Causal diagrams, or Directed Acyclic Graphs (DAGs), provide a visual framework for understanding causal relationships. They help distinguish genuine causal effects from misleading correlations.

Example: Sleep Quality

Consider the relationship between bedroom temperature and sleep quality. How can we determine if lowering your thermostat actually improves sleep?

In this diagram, arrows represent causal influences. We can see that bedroom temperature directly affects sleep quality. However, there might be confounding factors (like seasonal allergies) that affect both your temperature preference and sleep quality.

By properly accounting for these confounders in our analysis, observational data can reveal the true causal relationship between temperature and sleep quality—even without conducting a randomized experiment.

Real-World Applications of Causal Inference

The Smoking-Cancer Link

A classic example where observational data triumphed. In the mid-20th century, scientists established that smoking causes lung cancer without ever conducting an RCT (which would have been unethical). They used careful causal reasoning to rule out alternative explanations for the strong correlation they observed.

Sleep Environment Optimization

At Serif, we apply causal inference to your personal health data. For example, by analyzing patterns in your sleep quality across different bedroom temperatures while accounting for confounding factors like seasonal changes or stress levels, we can determine the optimal temperature range for your unique physiology.

Nutritional Impacts on Cognition

While RCTs on nutrition are challenging (people don't stick to assigned diets for long periods), causal inference methods allow us to determine how specific nutrients affect your cognitive performance by carefully analyzing observational data from your daily life.

Causal Effect Explorer

Use this interactive tool to explore how different factors might affect your own wellbeing, based on our causal models trained on thousands of users.