Data Exploration: Understanding Your Data Before Causal Discovery

Exploring a dataset before running causal discovery algorithms is often an important first step. Data exploration helps clarify which modeling assumptions are plausible and which methods are likely to be appropriate for the data at hand.

This page describes ways to inspect data using Tetrad’s Data Box, and how those observations commonly inform later choices when using Grid Search.

1. Load and Inspect Your Data

After loading a dataset into a Data Box, it is useful to note a few high-level characteristics:

  • Are the variables continuous, discrete, or mixed?

  • Are there missing values?

  • How many variables are there relative to the sample size?

These properties often influence which algorithms, tests, and scores are suitable in later stages of analysis.

At this stage, the goal is not to resolve every modeling decision, but to understand which assumptions seem reasonable to explore.

2. Review Variable Types

In the Data Box, look at the columns of th edata to determine:

  • Variable names

  • Data types (continuous vs. discrete)

If there are discrete varaibles, consider constructing a Parametric Model (Bayes or Hybrid) to determine:

  • Number of categories (for discrete variables)

This information matters because different methods rely on different assumptions:

  • Continuous data supports tests such as Fisher-Z and related approaches.

  • Discrete data requires discrete tests and scores.

  • Mixed data may call for hybrid or specialized methods.

Later, Grid Search automatically restricts available tests and scores based on these data types.

3. Examine Marginal Distributions with Histograms

Histograms in the Data Box allow you to inspect individual variables:

  • Are distributions roughly symmetric or strongly skewed?

  • Are there heavy tails or extreme outliers?

  • Do discrete variables have sparse or unbalanced categories?

These features can affect:

  • The behavior of linear-Gaussian tests

  • Sensitivity to outliers

  • Whether transformations or robust alternatives are worth considering

At this point, it is usually enough to note potential issues rather than committing to a specific test.

4. Explore Pairwise Relationships with the Plot Matrix

The Plot Matrix provides a visual overview of pairwise relationships:

  • Scatterplots for continuous variables

  • Apparent linear or nonlinear trends

  • Clustering or gaps that may suggest latent structure or selection effects

Visual inspection often reveals structure that is difficult to summarize with a single statistic. Patterns such as strong curvature or stratification can indicate that purely linear assumptions may be restrictive.

5. Consider Linearity and Gaussianity (Informally)

Many commonly used independence tests rely on assumptions such as linearity and Gaussian noise.

When examining the data, it can be helpful to consider:

  • Whether relationships appear approximately linear

  • Whether distributions are roughly Gaussian

  • Whether there are clear departures (e.g., strong curvature or multimodality)

These observations can guide whether linear-Gaussian methods are likely to be adequate or whether nonparametric alternatives should be included in Grid Search.

6. Reflect on Causal Sufficiency and Latent Variables

Based on domain knowledge and observed patterns, you may wish to consider questions such as:

  • Are important common causes likely to be unmeasured?

  • Do some associations appear potentially spurious?

  • Is selection bias or conditioning likely to play a role?

These considerations often inform whether the analysis targets:

  • A DAG or CPDAG, assuming causal sufficiency, or

  • A PAG, allowing for latent confounders and selection effects

Perfect certainty is rarely possible; the aim is to adopt a defensible starting point.

7. Clarify Your Modeling Goals

Before running searches, it can help to clarify what kinds of conclusions are of interest:

  • Adjacencies (which variables are connected)

  • Partial orientations (arrowheads where identifiable)

  • Fully oriented models under stronger assumptions

Different goals naturally call for different levels of modeling complexity and diagnostic checking.

8. Moving Forward

Once you have:

  • Identified variable types,

  • Noted distributional and relational features,

  • Reflected on causal sufficiency,

you are well positioned to move on to method selection and systematic exploration using Grid Search.

Careful data exploration often makes later results easier to interpret and evaluate.

Practical Notes

  • Even brief exploration can reveal features that affect method choice.

  • Visual tools often complement numerical summaries.

  • Let observations inform modeling decisions where possible.

  • Recording early observations can be helpful when interpreting results later.