A platform to capture mental models from thousands of stakeholders. Create surveys, collect responses, and discover patterns—all in one place.
Companies spend billions on surveys, focus groups, and consultants—but they only get opinions. They don't understand how people are organizing information in their heads.
Example: A survey tells you "60% support climate policy." But why? Some see economic benefits. Others see moral duty. Others see system collapse. Same opinion, completely different thinking.
Surveys ask questions you think are important. They don't show you what stakeholders think is important and how it all connects. You can't design effective messages without understanding their mental structure.
Get Causality measures how people organize information—their mental models at scale
Get Causality is in closed beta — all signups currently go through the waitlist. Tell us about your use case so we can prioritize onboarding based on your needs.
Join the waitlist for beta access and founding member pricing
Try the live demo and see how it works
We measure how people organize information in their heads—their mental models. Surveys tell you opinions. We show you how they're thinking about the problem.
A mental model is how someone internalizes cause-and-effect relationships. It's their personal theory of how things work—and different people can have completely different mental models about the same topic.
Join the waitlist → Create a survey or import data → Share a link → Platform aggregates responses → Run analyses with one click → Export publication-ready results.
Yes! Try the live demo to see how it works. Get Causality is in closed beta, so full access is via the waitlist — no billing during beta, and founding members lock in 30% off for two years after launch.
The only platform offering end-to-end research: from concept design to large-scale data collection to AI-powered analysis
Facilitated concept elicitation with stakeholders. Design your FCM survey instrument.
Explore Pricing →Deploy FCM surveys to thousands. Collect mental models at scale with our specialized survey builder.
See Survey Features →Automatic model generation, multi-model statistics, QAP, Mantel, GraphSAGE, and publication-ready exports.
See Analysis Tools →Causal modeling reveals how people reason about cause and effect—not just what they think, but the mental models behind their thinking. Fuzzy Cognitive Mapping (FCM) is the tool that makes it scalable, turning qualitative reasoning into quantifiable networks you can analyze at the scale of thousands.
Causal modeling asks stakeholders to draw the cause-and-effect relationships they see in a system. Instead of asking predetermined questions, you let people show you what factors matter and how they connect. The result is a quantifiable map of their reasoning—their mental model.
How it works: People identify the factors they think matter (like "pollution," "biodiversity," or "economic growth") and draw arrows showing how they influence each other. Get Causality uses Fuzzy Cognitive Mapping to turn these drawings into weighted, directed graphs that can be aggregated, compared, and analyzed mathematically.
Why it matters: Two people can have the same opinion but completely different reasoning. One person might see simple cause-and-effect. Another might see complex feedback loops. Traditional surveys can't capture this difference. Causal modeling does—and with FCM-based surveys, you can do it at the scale of thousands.
Follow a study from concept design to AI-powered insights — no account required
Experts and stakeholders collaborate to identify the key concepts and causal relationships in the system. The model builder is used to sketch initial causal maps and build shared understanding.
Research Question
How do different stakeholder groups perceive the causal relationships driving the energy transition?
Through expert panels, literature review, and stakeholder input sessions, the research team identified 19 key concepts and mapped initial causal relationships. The single-model builder captures shared knowledge before deploying at scale.
In the full platform: Run expert facilitation workshops, use the drag-and-drop model builder, and iterate on your study design before deploying.
The survey goes out to 1,200 stakeholders across four groups. Responses stream in and are automatically converted to individual mental models.
If Carbon Pricing Policy were to increase, how would it affect Economic Growth? Would it...
Not all items may be connected. Select “No Effect” or “N/A” if you believe there is no meaningful relationship.
Respondents rate the direction and strength of each causal relationship using a 7-point scale
Scientists group has only 94 responses. Need more data? The platform can generate synthetic FCMs conditioned on group demographics using SN-GAN augmentation (Miyato et al., 2018) to explore potential patterns in underrepresented groups. Synthetic models are flagged separately and should be validated against held-out samples.
In the full platform: Build custom surveys with branching logic, deploy via email/SMS/QR codes/custom links, track responses in real-time with automated reminders, and quality control with built-in verification.
968 completed models are aggregated and compared. The platform reveals where stakeholders agree and where they fundamentally diverge.
Renewable Investment → Job Creation
96% of models (929/968), mean weight +0.74 (SD 0.07)
Carbon Pricing → CO2 Emissions
93% of models (900/968), mean weight -0.71 (SD 0.09)
Fossil Fuel → CO2 Emissions
91% of models (881/968), mean weight +0.76 (SD 0.08)
Carbon Pricing → Economic Growth
Scientists +0.48 vs Industry -0.65 (Kruskal-Wallis H=18.4, p<0.001)
Regulatory Framework → Innovation
Policymakers +0.58 vs Industry -0.42 (H=14.2, p=0.003)
Fossil Fuel → Energy Security
Public +0.74 vs Scientists -0.48 (H=21.7, p<0.001)
Scientists and Policymakers share the most similar mental models (r=0.74, p<0.001, large effect). Industry diverges most from the aggregate (r=0.31, p=0.03, medium effect). Based on 5,000 permutation tests across 19-concept networks.
Scientists' models average 42.8 edges (density 0.25) with 6.3 feedback loops. Public models average 21.6 edges (density 0.13) with 1.4 loops. Higher domain expertise correlates with more complex causal reasoning (Spearman rho=0.68, p<0.001).
95% CI across 999 bootstrap resamples. Most stable edge: Renewable Investment → Job Creation [0.69, 0.81]. Least stable: Fossil Fuel → Energy Security [-0.15, 0.58] — high disagreement across groups.
In the full platform: Run 25+ statistical analyses including Mantel tests, structural equivalence, motif detection, relationship discovery, Hedges' g effect sizes with Holm/FDR correction, split-half reliability, sample adequacy testing, and segment comparison across any demographic variable.
Go beyond descriptive statistics. AI and deep learning reveal hidden causal structure, optimal interventions, and predictive insights across the full dataset.
Adjust intervention strengths and see predicted system-wide impacts in real time.
AI Recommendation (Q-Learning)
Optimal policy across 19-concept network: Carbon Pricing = 0.7, Renewable Investment = 0.8, Public Awareness = 0.6. Predicted system-wide impact 3.4x higher than baseline with indirect effects through Storage, Consumer Behavior, and Trade propagating across 6 feedback loops.
ML analysis identified 4 distinct stakeholder archetypes: Techno-Optimists (28%, emphasize Innovation → Growth → Storage), Policy Advocates (32%, emphasize Regulation → CO2 Reduction), Market Realists (22%, emphasize Trade → Costs → Consumer Behavior), and Status Quo Defenders (18%, emphasize Fossil Fuel → Energy Security). BIC: 2,841.
SN-GAN generated 300 synthetic Scientist models conditioned on group demographics. Effective sample boosted from n=94 to n=394. Bootstrap CI for Public edges narrowed by 44%. KL divergence between real and synthetic distributions: 0.07 (excellent fidelity).
In the full platform: Run GraphSAGE node embeddings, transformer attention analysis, GAN-based synthetic data generation, Monte Carlo sensitivity analysis, Bayesian uncertainty estimation, and more — all in your browser.
Complete research toolkit: Survey builder, data collection at scale, and research-grade analysis—all in one platform
Start with expert facilitation or design your own study
Expert-facilitated concept elicitation workshops. We help you design optimal FCM surveys by working with key stakeholders to identify critical concepts and relationships.
The only survey platform built for FCM data collection
"Complexity is an emergent property of aggregation, not a prerequisite for participation."
Participants contribute simple mental models. Complexity emerges when you aggregate hundreds or thousands of responses.
Build specialized FCM surveys with our drag-and-drop interface. Add concept lists, configure edge weights, and design the exact mental model capture you need. No other survey platform can do this.
Send surveys via email, SMS, or custom links. Embed on your website or share QR codes. Respondents can save progress and return later. Automated workflows trigger actions on completion, quality flags, or quota limits. Balanced concept coverage tracking, scheduled sends, and real-time response management. Scale from 10 to 1,000+ participants.
Each survey response is saved as an individual FCM and instantly combined into an aggregate model. No manual data entry, no Excel exports, no reformatting. No other platform does this—go from raw responses to publication-ready multi-model analysis instantly.
Monitor responses in real time with a live dashboard. Track completion rates by group, flag low-quality submissions automatically, set quota limits, and export clean datasets on demand. Full visibility into your data collection as it happens.
Research-grade tools for professional FCM analysis
Compare up to 1,500+ participant models simultaneously. QAP tests, Mantel analysis, edge frequency, structural equivalence, motif detection, relationship discovery, and concept selection analysis. Segment and compare how different stakeholder groups think with multi-level group comparison.
Split-half reliability, sample adequacy testing, formal meta-analysis with forest plots, consensus measurement, graph distance metrics, Hedges' g effect sizes with Holm/FDR correction, and Bayesian uncertainty with credible intervals on edge weights. 25+ analysis methods for research-grade results.
GAT and GraphSAGE models with attention mechanisms, transformer analysis, causal discovery, and GAN-based synthetic data generation. Discover hidden patterns and predict network behavior using cutting-edge deep learning.
Identify influential concepts with degree, betweenness, closeness, and eigenvector centrality. Find leverage points and key drivers in your system.
Run "what-if" simulations with FCM propagation. Clamp concepts to fixed values, iterate to equilibrium, and visualize how interventions cascade through the network. Compare baseline vs. intervention outcomes.
Drag-and-drop interface for creating FCM networks manually. Add nodes, draw weighted edges, and visualize causal relationships in real-time with interactive network layouts.
Designed for hundreds to thousands of respondents. Cultural consensus measurement, split-half reliability, power analysis, temporal tracking, multi-dataset merge with column harmonization, and per-group comparison across every analysis.
Find optimal intervention strategies using reinforcement learning. Q-learning identifies which concepts to activate or suppress for maximum impact on your target outcomes.
GAN-based behavioral prediction modeling. Generate synthetic stakeholder responses conditioned on demographics to predict how new populations would think about your problem.
Track how mental models evolve over time. Detect concept drift, structural change, and network evolution across multiple data collection periods.
Everything you need for professional research
Export high-resolution network visualizations, statistical reports, and interactive plots. Automated Word (.docx) and PDF report generation from analyses. CSV, Excel, SPSS, GraphML, PNG, and PDF formats supported. Ready for journals and presentations.
2FA (TOTP & email), WebAuthn/passkey login, SSO (SAML 2.0 & OIDC), AES-256 encryption, CSRF protection, intrusion detection, and automated security scanning. GDPR compliant with full data export and deletion.
FCM is used across industries to solve complex problems and make better decisions
Model climate change impacts, ecosystem dynamics, and conservation strategies. Analyze stakeholder perspectives on natural resource management and identify leverage points for policy intervention.
Map disease transmission pathways, healthcare system dynamics, and social determinants of health. Compare stakeholder mental models to identify intervention strategies.
Evaluate policy impacts across multiple dimensions. Capture diverse stakeholder perspectives to build consensus and identify unintended consequences before implementation.
Model organizational dynamics, strategic risks, and decision-making processes. Compare executive perspectives to align understanding of business challenges.
Analyze fishing satisfaction, habitat threats, and conservation solutions. Model stakeholder knowledge to inform sustainable fisheries management.
Conduct participatory research with publication-ready visualizations. Compare mental models across demographics using rigorous statistical methods.
Get answers to common questions about Get Causality
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, and group comparisons. Machine Learning: ML ensemble, SHAP explainability, latent class analysis, and cross-validation. Causal Discovery: PC algorithm, GES, LiNGAM, and scenario modeling. Predictive: GAN-based synthetic data, RL policy optimization, and intervention simulation. All with automated visualizations, interpretation guides, Word/PDF reports, and exportable results. See the Live Demo for examples.
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 is available via TOTP or email codes, and all users can configure IP allowlists. GDPR compliant with full data export and account deletion. If you have specific IRB or institutional requirements, contact us to discuss custom data handling agreements.
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 are logged with user ID, IP, and timestamp for IRB and compliance review. See the Enterprise page for the full security overview.
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 (see the "Live Demo" tab) 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 format: Prasky, E. (2026). Get Causality: Browser-based fuzzy cognitive mapping research platform [Software]. https://get-causality.com. BibTeX and in-text citation formats are available 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 TensorFlow.js 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 us at support@get-causality.com. If you want to schedule a demo for your team, contact sales@get-causality.com. Paid users get technical support with a 24-hour response time. Beta users get priority support and direct access to our development team.
Transparent tiers designed for research teams. Prices shown are post-launch rates — beta waitlist members lock in a founding-member discount.
Perfect for learning FCM basics
Requires .edu verification
For educational use only
Multi-Model + Meta Models
For educational use only
Unlock Predictive Cognition
For educational use only
Commercial license for consultants
Full platform + commercial license
For research teams & organizations
Reach out for pricing
We offer workshops, survey services, and bundled packages for teams of any size.
Learn About EnterpriseAll plans include basic analysis, unlimited responses per survey (100 on Free), and CSV/PNG export.
| Academic (Edu License) | Commercial License | 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 | — | — | — | ✓ | — | ✓ | ✓ | ✓ |
| 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 |
Have questions? We'd love to hear from you.
See how Get Causality transforms data into insights