01
Black-box predictions
A prediction can rank risk without clarifying which intervention might change the outcome.
Open-source private-use prototype
Causal AI prototype for private analysis and learning
aitiolin is a browser-based interface for interactive DAG building, identification analysis, causal effect estimation, robustness testing, and time-series causal analysis. It helps users move from “what correlates with churn?” to “what may happen if we actually change something?” in a transparent, non-production setting.
- Interactive graph canvas with human-reviewed assumptions
- Identification checks before estimating an effect
- Robustness dashboard for stress-testing results
- White-box behavior for explainable analysis
Identification panel
Backdoor paths, admissibility checks, and adjustment sets.
Treatment: Online Security
Outcome: Churn
Status: Identifiable under the current DAG
Outcome: Churn
Status: Identifiable under the current DAG
Robustness
Parallel estimators, placebo tests, subsamples, and sensitivity sweep.
Robustness score: 82 / 100
Composite diagnostic score based on the checks shown below. It is not a confidence level, probability of causal correctness, or certification.
Composite diagnostic score based on the checks shown below. It is not a confidence level, probability of causal correctness, or certification.
Why this matters
Causal analysis should make assumptions visible before interpretation
Many AI projects produce scores without clearly showing why a result appears or whether the underlying assumptions are defensible. For private users and researchers, that makes intervention reasoning harder, not easier.
02
Correlation is not intervention
Any intervention strategy needs a causal question: what happens if we change pricing, support, or retention spend?
03
Shared assumptions matter
Visual graphs allow domain experts, analysts, and other reviewers to examine assumptions together.
Prototype
One open-source GUI prototype for the full causal workflow
Instead of stitching together multiple libraries and notebooks, this open-source prototype concentrates graph drafting, identification, estimation, and robustness into a single browser-based workflow for private use.
A
Interactive DAG builder
Drag variables onto a canvas, create directed edges, and persist graph state across sessions.
B
Graph Copilot
Get AI-assisted edge suggestions with reviewable evidence and accept/reject decisions.
C
Identification analysis
Check DAG validity, admissibility, adjustment sets, and candidate identification strategies.
D
Effect estimation
Run estimators such as linear regression, propensity score matching, and weighting.
E
Robustness dashboard
Compare estimators, run refutations, and inspect a compact robustness score.
F
Time-series mode
Analyze rolling-window stability where a time dimension is available.
Workflow
From CSV to causal insight in eight steps
The workflow is structured so users inspect assumptions before effect estimation and robustness before interpretation.
Upload a CSV
Bring in a private dataset and inspect available variables.
Build the graph
Place nodes on the canvas and draw directed edges.
Review Copilot suggestions
Use AI assistance, then keep humans in control of final structure.
Run identification checks
Validate whether the treatment–outcome question is answerable.
Select treatment and outcome
Specify the intervention question you want to analyze.
Estimate the effect
Run one or more estimators against the causal query.
Stress-test the result
Inspect placebo tests, dummy outcomes, subsamples, and sensitivity.
Interpret responsibly
Use estimates and robustness context for learning, not automated or business decisions.
Use case
Example scenario: can retention changes reduce churn?
In a churn setting, a prediction model can tell you which customers are risky. A causal workflow can help you ask whether an intervention - retention spend, service upgrades, online security, or support changes - may reduce churn under explicit assumptions in a private analysis context.
Q
The question
Would increasing retention or marketing spend actually reduce churn, or only correlate with it?
M
The model
Represent drivers of churn as a causal graph and make assumptions visible.
D
How to interpret the result
Compare the estimated effect, robustness checks, and practical context to document a hypothesis for further investigation. The result is not a recommendation to change marketing or retention spending.
Trust and governance
Designed for transparent causal exploration and hypothesis development
aitiolin is developed as an open-source prototype for private experimentation and learning, not for autonomous or operational decision-making. The graph, identification logic, and robustness checks are visible and reviewable.
aitiolin can support transparent, human-reviewed workflows, but legal compliance depends on the use context. This tool is intended for private use only
and is not intended for business decisions.
W
White-box behavior
The causal graph makes structural assumptions visible instead of hiding them in a black-box score.
H
Human-in-the-loop
Edge suggestions can be reviewed, accepted, locked, or rejected by a human user.
R
Robustness before interpretation
The dashboard encourages stress-testing before drawing conclusions from an estimate.
News
Latest changes from the aitiolin repository
Causality Agent introduced
- New opt-in Causality Agent panel profiles the uploaded dataset and flags quality issues (missingness, duplicates, outliers, collinear pairs).
- Agent proposes a reviewable, staged cleaning plan and, once applied, all analysis automatically uses the cleaned dataset.
- Agent suggests a causal model (edges, treatment/outcome candidates, estimator), LLM-assisted when an OpenAI key is set, with statistical heuristics as a fallback.
- New "Compare competing models" step re-estimates the effect under 2-3 alternative DAG variants (canvas, minimal, confounder-stressed) and reports a stability verdict so you can see how much the conclusion depends on the chosen graph.
- Suggested causal models now come with a "Why this model" reasoning section, and the LLM prompt was extended to explain the outcome, treatment, and biggest structural uncertainty.
- Estimation results include a plain-language "What this means" explanation covering effect direction, magnitude, and the adjustment set used, plus more detailed rationale for the recommended estimator.
Screenshots
Open-source prototype
Explore the repository and test it privately
aitiolin is an open-source prototype for transparent causal exploration and hypothesis development. It is intended for private use, experimentation, and learning, not for production or business decision workflows.