Allye - Tutorials

Causal Analytics
Core Engine

From basic statistical techniques to cutting-edge causal inference methods, all practical methods are supported.

For the most detailed specifications, please refer to our document.

Regression

Binary Classification

Propensity Score Matching

Causal Tree / Forest

Double Machine Learning

AB Test

DID

Time Series Analysis
& Forecast

Machine Learning

All essential techniques supported. Analyze in minutes

All practical Supervised, Unsupervised Machine learning tecnhiques

Perform Cross validation and other practical evaluation methods by 1 click

Data load

Drag files, connect to your databases and Google Sheets, and pull any data directly into your Python notebook

Bigquery, Redshift, MySQL, Postgresql, SQL server

Google sheet, csv, xlsx, tsv and others

Transform & Visualize

Handle routine prep with no-code nodes, and tackle any custom logic with AI-generated Python.

Filter, Extract, Aggregations, Merge, Sampling, Impute, Preprocess pipeline nodes for frequent ETL tasks

Bar plot, Box plot, Histogram, Scatter plot, Time series and lots other visualization nodes

Seamlessly connected Python jupyter notebook for custom logic / visualization

Bar plot

Line Plot

Pivot Table

Preprocess pipeline

Flexible Visualization

AI generates Python code to perform visualizations in notebooks for a wide range of requirements.

Example: Calibration plot

AI Assistant

Node Builder - Automatically generate data processing workflow

Understand Data and Variable Types

Node & Python generation and Execution

Loop on Errors

AI Reporting and Chat

Help interpret statistical results

Ask node usage and detail specs

Your Turn to Uncover the "Why".

The analysis you just read about isn't magic — it's what happens when powerful analytics become truly accessible.
Stop wrestling with tedious code and start exploring your data at the speed of thought.

Get Started with Allye