User Guide & Documentation
This guide explains how to prepare your data and use the Causal Exploring tool to run a causal analysis on your dataset.
Step 1: Prepare your CSV file
Your dataset must meet the following requirements before uploading to ensure successful analysis:
- Format: CSV file only.
- First row: Variable names (column headers). If your CSV includes row index numbers as the first column, you can leave the first header blank (e.g. starting with a comma like
,var1,var2). - Values: All data must be numeric (either continuous floats or integer codes). Any categorical text fields (e.g.
"male"/"female") must be pre-encoded into numeric values (e.g.,1/2). - Missing values: No blank cells or missing values (
NaN/NA) are allowed. Ensure your data is fully complete and clean beforehand. - Separator: Comma (
,) or semicolon (;) separators are both supported.
Step 2: Upload your dataset
Navigate to the Home page and drag and drop your CSV file into the upload zone, or click the zone to open a file selector. Only CSV files are accepted. Once uploaded, verify that your file is correctly detected before proceeding.
Step 3: Select an algorithm
Choose one of the three available causal discovery algorithms:
- PC-Stable — reliable and fast, good default choice
- MRPC — if you have prior knowledge about your variables
- MMHC — if your dataset is noisy or has many variables
Step 4: Run the analysis
Click the Run Analysis button. The tool will process your dataset and display the resulting causal graph on the Results page. Nodes represent your variables, and directed edges represent inferred causal relationships between them.
Note on data quality: Ensure that your theoretical framework supports the assumptions required by the selected algorithm before interpreting the results.