Allye Pro

No-Code Workflow for Causal Analysis & Decision Science.
Own-Your-Data. Run Locally.

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Allye Pro

Easy to Use

Simple and Intuitive No-Code workflow. Ready to use out of the box.

Best for Decision

From Statistical analysis to Causal Analysis. All you need for Decision Science.

No Subscription!

Buy it once. Use it forever. No recurring fees, no lock-in, no surprises.

Security First

Your data stays on your machine. No Internet, No Risk for confidential data.

Truly Interactive

Explore, adjust, and validate — all in real time. Every step is transparent, inspectable, and reproducible.

No Limits

AI generate Python Code. You handle No-Code. Flexibility meets Efficiency.

Easy and Productive

Drag and drop intuitive UI.
Responsive.
Learn more - Quick start

Decision Science

Rich Causal Inference Models. The "state-of-the-art" algorithm in minutes.
Extensive Statistical Analysis methods.

No Subscription!      

One purchase. Two devices. That’s it.  
Need dedicated support? Contact our enterprise team.

No Risk!

Your data stays on your machine.
No Shadow AI.
No Internet, No Risk.

No Limits

Python Notebook deep integration. You can seamlessly switch between writing code and using the Canvas.
Ollama-compatible Python Notebook Agent. Run LLMs locally.

Go Beyond Modeling
Decision Ready Report

While traditional tools stop at model training, Allye automates the critical "sanity check". We generate comprehensive reports—from residual diagnostics to causal validation—ensuring your results are statistically sound and actionable.

Regression Analysis

Coefficients with CI/p-values, VIF, bar plot, R²/AIC/BIC, pred vs actual, and residual diagnostics.

PSM

Before/after matching distributions, sample size impact, and balance checks (Love plot, SMD).

Double ML

Train/Test CV reports for 3 models, overlap coverage, ATE/ATT/ATC with stability, uplift/AUUC, and CATE.

Open and Clear Deal

Pro

$300

All widgets. Notebook Agent-ready.
See below for all capabilities.

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Contact us / Request Demo

‍Use Cases

Marketing

Measure True Effect

Every marketer faces a fundamental question: is my advertising working? Let's measure causal impact.

Product

A/B Test Deep-Dive

Unfortunately, A/B Test result was flat. What's next? Let's find causal insights and shape the strategy.

Product

Growth Unlocked

The Growth Team's Dilemma: From Guesswork to Glory with Allye

Research

Beyond the Surface

Go beyond correlations to uncover true causal drivers.

Learn More

Exploratory data analysis - "they questioned whether some customers are being assigned unjustified interest rates"
A/B Test Result Analysis - "Your A/B test for the new product feature has concluded. Let’s analyze the data to shape strategy"
Survey Analysis - "An airline has conducted a comprehensive passenger satisfaction survey. The goal is to identify customer needs and pain points"
Causal Inference - "Imagine you're a marketer who launched a newsletter. But you can't be sure if the newsletter is causing good lift."
ML predictive model prototyping - "The best way to build strong models is through rapid prototyping and iterative trial-and-error."
Trusted by professional scientists at
Allye Pro - Capabilities

Capabilities

Basic hypothesis testing

  • A/B testing
  • Multiple Comparison Correction: Bonferroni, Holm, Benjamini-Hochberg
  • t-Test
  • Paired t-test
  • ANOVA
  • Equivalence testing
  • Chi-squared test
  • Confidence interval

Advanced regression

  • Linear regression
  • LASSO
  • Ridge
  • Regression Model Report: Coefficient, Confidence Interval, P-value, VIF
  • Regression Metric Report: R2, Adj R2, MSE, RMSE, AIC, BIC
  • Regression Visualization Report: Predicted vs Actual Scatter Plot, Coefficient Bar Plot
  • Regression Residual Analysis Report: Residuals vs Fitted, Normal Q-Q, Scale-Location, Residual vs Leverages
  • Logistic regression
  • Logistic regression(L1)
  • Logistic regression(L2)
  • Binary Analysis Model Report: Coefficient, Confidence Interval, P-value, Odds Ratio, VIF
  • Binary Analysis Metric Report: Accuracy, Precision, Recall, F1 Score, AUC, Log Loss
  • Binary Analysis Visualization Report: Coefficient Bar Plot, ROC Curve, Confusion Matrix
  • Poisson regression
  • Poisson Model Report: Coefficient, Confidence Interval, P-value, IRR, VIF
  • Poisson Metric Report: Deviance, Pseudo-R2(McFadden), Log-Likelihood, AIC, BIC, Dispersion
  • Poisson Visualization Report: Coefficient Bar Plot, Predicted vs Actual Scatter Plot, Residuals Diagnostics, Calibration Curve / Gain

Multivariate analysis

  • Correlation analysis
  • Principal components analysis (PCA)
  • Factor analysis
  • Partial least squares (PLS)
  • Correspondence analysis
  • Linear Discriminant analysis
  • t-SNE

Causal Inference

  • Propensity Score Matching
  • PSM Model Diagnostics Metric: AUC, Accuracy, Log Loss
  • PSM Model Diagnostics Model: Coefficient, Odds Ratio, Importance
  • PSM Model Matching Preview: Before / After Matching Score Distribution, Sample Size
  • PSM Model Balance Evaluation: Love Plot, SMD Balance Metrics
  • Linear Double Machine Learning
  • Causal Forest Double Machine Learning
  • DML Propensity Score Diagnostics Report: Train / Test dataset's Score Distribution Visualization
  • DML Treatment Model Summary Report: Train / Test dataset's AUC, Log Loss Brier Score, Treatment Calibration plot, SHAP Visualization
  • DML Outcome Model Summary Report: Train / Test dataset's AUC, Log Loss Brier Score, Outcome Calibration plot, SHAP Visualization
  • DML Effect Model Summary Report: Train / Test dataset's ATE, ATT, ATC, Fold ATE Variance, Overlap Coverage, AUUC, Effect Model Coefficient Visualization, Cross-Fold CATE Stability Visualization, Train / Test Uplift Curve, CATE Distribution
  • Causal Forest
  • DID
  • DID Report: Time Series Visualization, Event Study plot, Residual Analysis Report, Parallel Trend Test, Parallel Trend Model Metric (R2, Adj R2, F-statistics)

Time series

  • Trend
  • Seasonality
  • Residual
  • Moving average
  • Prophet Time Series Forecast

Machine Learning

  • Decision Tree
  • Random Forest
  • AdaBoost
  • Gradient Boosting
  • XGBoost
  • kNN
  • Linear Regression
  • Logistic Regression
  • SVM
  • Neural Network
  • Naive Bayes

Evaluation

  • Cross Validation
  • Confusion Matrix
  • ROC Analysis
  • Performance Curve
  • Calibration Plot
  • Permutation Plot
  • Parameter Fitter

Unsupervised

  • MDS
  • Hierarchical Clustering
  • k-means
  • Louvain Clustering
  • DBSCAN
  • Outliers - One Class SVM
  • Outliers - Isolation Forest
  • Neighbors

Segmentation

  • Clustering
  • Hierarchical clustering
  • K-means clustering
  • Self-organizing maps (SOM)
  • Discriminant analysis
  • Multidimensional scaling

Explanatory modeling

  • ANOVA
  • Regression

Survey analysis

  • Factor analysis
  • Correspondence analysis
  • Odds ratios
  • Categorical data analysis

Survival Analysis

  • Kaplan-Meier Plot
  • Cox Regression
  • Stepwise Cox Regression
  • Cohorts
  • Survival Nomogram

Data Manipulation

Data Source

  • File(csv, xlsx, xls, tsv) Read
  • DB(PostgreSQL, MySQL, SQLServer, BigQuery, Redshift) Connector
  • SQL Executor
  • Google SpreadSheet
  • Allye Data Receiver / Transmitter

Transform

  • Python Notebook
  • Data Sampler (Fixed proportion / sample size random sampling, bootstrap sampling, sample with replacement, cross validation, stratify sample)
  • Select Rows / Filter data
  • Select Columns
  • Merge Data (left join, inner join, outer join)
  • Group By aggregation
  • Pivot Table aggregation
  • Preprocess pipeline(Discretize Continuous Variable, Continuize Discrete Variable, Impute Missing values, Normalize Feature, Randomize, PCA, Remove Sparse Features)
  • Formula - Construct new features
  • Edit Domain
  • Transpose
  • Concatenate
  • Melt

Visualize

  • Box Plot
  • Distribution / Histogram
  • Scatter Plot
  • 3D Scatter Plot
  • Bar Plot
  • Line Plot
  • Heat Plot
  • Sankey Flow
  • Feature Statistics
  • Geo Map
  • Linear Projection
  • Violin Plot
  • Sieve Diagram
  • Mosaic Display
  • Free Viz
  • Radviz
  • Clustering Heatmap
  • Venn Diagram
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