01
Capture Expert Knowledge
Convert qualitative interviews and surveys into formal causal models. No PhD in graph theory required.
Get Causality captures the mental models behind survey answers — at the scale of thousands. Map reasoning. Compare segments. Simulate interventions.
Same instrument, four research jobs. Each backed by peer-reviewed methods, automated for scale.
Convert qualitative interviews and surveys into formal causal models. No PhD in graph theory required.
Aggregate individual mental models into population-level diagrams. See where consensus emerges and where it fractures.
Run what-if simulations on the model. Quantify how a policy lever ripples through the system before you ship it.
Filter by demographic, role, or expertise. Surface the structural disagreements that drive policy stalemates.
Self-service end-to-end. Optional facilitated workshops at the start.
RL-based Q-learning over an 8-concept climate-policy network. Drag the levers — system-wide impacts update in real time, translated back to the original 7-point Likert response scale so policy shifts are interpretable to non-technical stakeholders.
Optimal policy across the 8-concept climate-policy network: Carbon Pricing 0.7, Renewable Subsidies 0.8, Public Awareness 0.6. Predicted system-wide impact 3.4× higher than baseline — indirect effects propagate through Grid Capacity, Job Transitions, and Energy Affordability across 3 feedback loops in the discovered causal structure.
Domain-neutral platform. Each card links to a representative study with concept set, segment design, and figures.
Causal modeling doesn't replace your methods stack. It answers a different question. Regression tells you the average answer. Causal modeling tells you how people got there.
Tells you the average answer across everyone. It doesn't tell you how anyone got there.
You write down a theory and SEM checks if the data fits it. Causal modeling asks each person what their theory is, then compares them.
Builds one diagram that fits the whole group. Causal modeling keeps every person's diagram, so you can see who agrees and who doesn't.
An expert draws the loops and runs the simulation. Causal modeling lets thousands of regular people draw their own, so you see which loops people actually believe.
Two people can give the same answer for completely different reasons. Surveys average that away. Causal modeling shows the reasons.
Prices shown are post-launch rates. Founding members lock in 30% off monthly (≈10% off annual) for two years after public launch.
Learning FCM basics, run a pilot
Requires .edu verification
Multi-Model + Meta Models
Unlock Predictive Cognition + LLM FCM Builder
Consultancies, small teams
Full platform + commercial license
Research teams & organizations
Reach out for pricing
All plans include basic analysis, unlimited responses per survey (100 on Free), and CSV/PNG export.
| Academic (Edu License) | Commercial | Enterprise | ||||||
|---|---|---|---|---|---|---|---|---|
| Feature | Free | Academic | Acad Plus | Acad Pro | Researcher | Professional | Team | Enterprise |
| Monthly Price | $0 | $249 | $349 | $449 | $499 | $999 | $1,999 | Custom |
| Multi-Model Analyses | — | |||||||
| Meta-Model Analyses | — | — | — | |||||
| Predictive Cognition | — | — | — | — | ||||
| LLM FCM Builder | — | — | — | |||||
| Goal-Seek Optimizer | — | 3 modes | All modes | All modes | 3 modes | All modes | All modes | All modes |
| Stored Surveys | 1 | 10 | 25 | 50 | 10 | 50 | 100 | Unlimited |
| A/B Testing & White-label | — | — | — | — | — | |||
| Team Members | 1 | 1 | 1 | 1 | 1 | 3 | 10 | Unlimited |
| SSO / SAML | — | — | — | — | — | — | — | |
| Support | Community | Email 48h | Email 24h | Priority 24h | Priority 24h | Email 24h | Priority 12h | Dedicated |
We measure how people organize information in their heads — their mental models. Surveys tell you people's opinions. We show you how they're thinking about the problem. Do they see it as one thing causing another in a line? Or do they see a complex web of interconnected causes? That difference matters because you can't convince someone by arguing against their mental structure.
A mental model is how someone internalizes cause-and-effect relationships in their head. It's their personal theory of how things work. For example, one person might think "more regulation → less pollution → better health." Another might think "more regulation → higher costs → job losses → worse health." Same topic, completely different mental models. Understanding these differences is key to effective communication and policy design.
Three things: (1) What concepts people connect — do they link "climate change" to "fishing" or not? (2) How they connect them — does A cause B, or do they affect each other? (3) How complex their thinking is — simple chains vs. feedback loops. The output is a map of their thinking, plus statistics showing if different groups think fundamentally differently.
(1) Join the waitlist — Get Causality is in closed beta; no billing yet. (2) Create a survey using our drag-and-drop builder, or import existing data. (3) Share a link — participants draw their mental models in their browser (no app install). (4) Platform aggregates responses into analyzable networks automatically. (5) Run analyses with one click — centrality, clustering, group comparisons. (6) Export publication-ready visualizations and statistics.
Anyone who needs to understand stakeholders before making big decisions: consultants doing stakeholder analysis for clients, companies launching new products or policies, governments designing public policy, and researchers studying how people understand complex issues. All of them currently guess at how stakeholders think. We measure it.
Surveys ask questions you think are important. We let people tell us what they think is important and how it all connects. Example: A survey asks "Do you support climate policy?" and gets 60% saying yes. Our platform shows you why they support it — some see economic benefits, others see moral duty, others see system collapse. Same opinion, completely different thinking. You need to know the why to design messages that work.
Traditional FCM analysis can take weeks between getting survey responses and having results — most of that time spent on data cleaning, reformatting, and writing R scripts. Get Causality automates this workflow. Import your data, select your analyses, and get results in minutes instead of weeks. The platform is designed to handle thousands of participant models with demographic comparisons and automated visualizations.
At public launch the Free plan will include the model builder (up to 50 concepts), 1 survey, basic single-model analysis, and CSV/PNG export — built for students, small pilot projects, or evaluating the platform. We're in closed beta right now; join the waitlist for early access and founding-member pricing.
We support most common FCM data formats. You can import survey exports (CSV or JSON), standard FCM JSON files, Excel adjacency matrices, or just paste directly from a spreadsheet. The platform automatically detects your data format and handles the conversion, so you don't need to manually reformat anything.
40+ analysis methods across four categories. Network Analysis: centrality, clustering, structural equivalence, motif detection, QAP tests, group comparisons. Machine Learning: ML ensemble, SHAP explainability, latent class, cross-validation. Causal Discovery: PC algorithm, GES, LiNGAM, scenario modeling. Predictive: GAN-based synthetic data, RL policy optimization, intervention simulation. All with automated visualizations, interpretation guides, Word/PDF reports.
Yes. All data is encrypted in transit (TLS) and at rest (AES-256-GCM). Passwords are hashed with bcrypt. A built-in PII filter automatically strips personally identifiable columns from uploaded data before it reaches any analysis pipeline. Two-factor authentication via TOTP or email codes. IP allowlists. GDPR compliant with full data export and account deletion. Custom data-handling agreements available for IRBs with specific requirements.
Enterprise and Team tiers add institutional SSO (SAML 2.0 and OpenID Connect), per-institution 2FA enforcement, WebAuthn/FIDO2 passwordless login, trusted device management, comprehensive audit logging, and intrusion detection alerts. All security events logged with user ID, IP, and timestamp for IRB and compliance review.
You can pay monthly (cancel anytime) or annually (save 20%). All plans include feature updates as we add them. If you have a team of 10 or more, contact us about enterprise pricing.
Yes. Explore the live demo above with example models — no account needed. When you're ready, join the beta waitlist for early access and founding-member pricing locked in for two years after public launch.
Yes. We appreciate (but do not require) a citation. Suggested APA: Prasky, E. (2026). Get Causality: Browser-based fuzzy cognitive mapping research platform [Software]. https://get-causality.com. BibTeX and in-text formats in the Terms of Service.
All analyses run entirely in your browser. Your data never leaves your machine for computation — the platform uses Web Workers and in-browser WebAssembly (Pyodide), with WebLLM/WebGPU for language-model features, to perform network analysis, machine learning, and causal discovery client-side. This means no data is sent to external servers for processing, which simplifies IRB compliance and data governance.
Yes. Professional and Team plans include shared team access with role-based permissions. Team members can view surveys, run analyses, and export results from a shared workspace. Enterprise plans offer unlimited team members with SSO integration.
Just reach out. For general questions, email [email protected]. To schedule a demo for your team, contact [email protected]. Paid users get technical support with a 24-hour response time. Beta users get priority support and direct access to our development team.
Closed beta — no billing yet. Founding members lock in 30% off monthly (≈10% off annual) for two years after public launch.