Grid Search Box (Data)
This page describes how the Grid Search box behaves when it is connected to a Data box.
In this mode, Grid Search performs systematic comparison of causal discovery methods on a fixed dataset.
No simulations are run in this mode. All results are deterministic and fully reproducible.
Purpose of Data-Based Grid Search
When operating on supplied data, Grid Search is designed to help you:
Compare algorithms, tests, scores, and parameter settings
Evaluate candidate models using diagnostics (e.g., Markov checking)
Identify simple, Markov-consistent models
Assess robustness of features across reasonable modeling choices
This is the recommended default workflow for causal discovery in Tetrad.
When This Mode Is Used
Grid Search automatically enters data mode when:
A Data box is connected to the Grid Search box, and
No simulation is selected.
In this case:
Simulation controls are hidden or disabled
Each algorithm–parameter combination is evaluated once
Results reflect only variation across modeling choices, not random variation
Basic Setup
To use Grid Search with data:
Load your dataset into a Data box
Draw an edge from the Data box to the Grid Search box
Open the Grid Search editor
The editor will configure itself for data-based comparison.
Algorithms Tab
In the Algorithms tab, you select:
One or more causal discovery algorithms
Required independence tests and/or scores
Parameter ranges for algorithms, tests, and scores
Parameter values may be entered as comma-separated lists.
Grid Search will evaluate all combinations of the selected parameters.
Only tests and scores compatible with the data type (continuous, discrete, mixed) are shown.
Table Columns Tab
In the Table Columns tab, you choose which quantities appear in the comparison table.
Available columns include:
Algorithm and parameter values
Model complexity measures (e.g., number of edges)
Diagnostic statistics (e.g., Markov check results)
When working from data, statistics that require knowledge of the true graph are not shown, since no ground truth is available.
Columns may be added, reordered, or removed using the Add and Manage buttons.
Comparison Tab
The Comparison tab controls how results are evaluated and displayed.
Key options include:
Comparison graph type (e.g., CPDAG or PAG)
Markov Checker test
Utility settings for ranking models
When you click Run Comparison, Grid Search:
Runs each selected algorithm for every parameter combination
Evaluates resulting graphs using selected diagnostics
Populates the comparison table with results
Progress and detailed output are shown in the Verbose Output tab.
Interpreting Results
Each row in the comparison table corresponds to a distinct model.
Typical analysis focuses on:
Whether the model passes Markov checking
Model complexity (e.g., number of edges)
Stability of features across nearby parameter settings
Rather than selecting the single highest-utility model, it is usually more informative to identify minimal models that pass diagnostics.
View Graphs Tab
After a comparison is complete, the View Graphs tab allows you to inspect individual output graphs.
Selecting a row in the table highlights the corresponding graph
Graph selections are remembered when the editor is reopened
This makes it easy to compare candidate models visually.
Notes and Best Practices
Sweep only a small number of meaningful parameters at a time
Prefer systematic comparison over isolated runs
Use diagnostics early to detect mismatched assumptions
Treat fragile edges and orientations with caution
Summary
When connected to data, the Grid Search box provides a structured, reproducible way to:
Explore algorithm and parameter sensitivity
Evaluate candidate causal models
Identify parsimonious models consistent with the data
This mode forms the backbone of Tetrad’s recommended causal discovery workflow.