Causal AI and LLMs are strong complements in product design.

• The math of causality can be explained and contextualized using an LLM’s world knowledge.

• The bullshit of an LLM can be corrected and deepened by causal structure and causal inference.

We’re building an innovative AI system + product architecture:

Causal Topology – Based Macro-Semantic Inference Engine

( working name: Abel Causal Engine )

The core idea: we don’t stay at the level where LLMs are best—language association. Instead, we intentionally “compress” the LLM into an entry router and a knowledge bridge, and hand real reasoning to a large-scale causal graph discovered by PCMCI.

This solves the most fatal problems in RAG systems—hallucination and logical gaps—because every output is backed by mathematically grounded causal probabilistic inference and do-calculus.

LLM/RAG vs. Abel Causal Engine

Not a Formal Design Doc — A Product × Engineering View

This isn’t a strict design document. It’s a product × engineering synthesis of how we reason using CKG (Causal Knowledge Graph) + LLM, and how we systematically address data sparsity and data silos.

The core logic:

• LLM as KG + “semantic router”

  It maps prediction markets, social media, and users’ long-tail, unstructured questions (e.g., “investing,” “social,” “buy a home,” “job hopping”) into financial-market entity anchors (tickers) with high precision.

• Control is transferred to the underlying causal graph (CG)

  This graph uses global financial markets as a holographic data encoder. It collapses everything (events, sentiment, supply chains) into public, high-frequency, continuous price–volume signals, systematically bypassing real-world sparsity, non-standard formats, and siloed datasets.

  In other words: we use the flow of money, volume, and statistics to fill in the gaps created by broken information pipelines.

• The system runs strict mathematical inference on the graph

  It uses do-calculus & interventions to lock in causal chains and compute mathematical outcomes.

• Finally, the LLM “decompiles” the math back into human insight

  It translates quantitative causal parameters into instant “aha” life insights, decision hacks, and genuinely interesting interpretations.

To show how we pierce through ordinary daily questions into a number-driven causal world, here are several deep inference examples across different dimensions.

Case Study 1: The “Compute–Energy War” Behind Hardware Consumption

Theme: Hidden links between consumer hardware and infrastructure bottlenecks

User question

“I want to build a high-performance desktop to run local LLMs. What GPU should I buy—does a 4090 make sense?”

1) Entry tickers (T₀)

• NVDA, AMD, DELL (servers/workstations)
  
  

2) Propagation + Surprise (Causal discovery)

• Obvious chain: NVDA price → GPU price (basic supply/demand)

• Abel causal discovery:
  
  

  • NVDA (volume) → (τ = 5 days) → VRT (Vertiv: cooling / datacenter thermal management)

  • VRT → (τ = 2 weeks) → CCJ (Cameco: uranium / nuclear) or TLN (Talen Energy: nuclear-powered datacenters)
    
    

• Surprise: When you think you’re optimizing GPU performance, the real parent-node constraints often aren’t yield—they’re power and cooling.
  
  

3) Math & intervention

• Intervention: do(NVDA_Demand ↑ 15%)

• Causal results (example):
  
  

  • VRT response coefficient β = 0.65 (high confidence, p < 0.01)

  • If AMD demand rises, the causal strength to VRT is only β = 0.12 (low significance)
    
    

• Inference: R² suggests ~30% of NVDA’s moves are explainable by downstream power scarcity expectations—a reverse-mechanism signal: energy constraints cap compute expansion.
  
  

4) Example UI output (Abel Insight Card)

Market metaphor

You think you’re buying GPUs. You’re actually going long electricity bills.

Causal link

NVDA (GPU) — (τ = 14d, β ≈ 0.6) → VRT (cooling) — (τ = 30d) → Utilities / Power

Abel inference

Over the past 3 months, PCMCI detected that NVDA shipment volatility leads VRT price with 92% confidence.

• Sharp: ~30% of today’s GPU premium is the market pricing in datacenter power shortages.

• Certain: The bottleneck has shifted from “chip yield” to cooling + power. Utilities often lag GPU heat by ~2 weeks.

• Fun fact: If GPUs feel expensive, look at Cameco (CCJ). Every Llama-3 training run is implicitly consuming that inventory.
  
  

Decision tip

Buying GPUs is consumption. If you also hold VRT or a utilities ETF, you hedge your “gold mining” cost by owning the “shovel sellers.”

Case Study 2: Lifestyle Decisions as a Bubble “Signal Light”

Theme: Creative labor displacement risk + speculative sentiment

User question

“My company wants to buy high-quality stock image library licenses. Is it worth it—any recommendations?”

1) Entry tickers (T₀)

• SSTK (Shutterstock), ADBE (Adobe), GETY (Getty Images)
  
  

2) Propagation + Surprise

• Obvious chain: SSTK down → competitors reprice

• Abel causal discovery:
  
  

  • SSTK (price drop) → (τ = 1 day) → WLD (Worldcoin: identity/UBI narratives)

  • SSTK (implied vol) → (τ = 0) → NEAR (NEAR: data availability / AI-chain narratives)
    
    

• Surprise: The collapse in stock-media equities is not “the economy is bad.” It’s a hyper-sensitive indicator of AI displacement rate—with strong causal coupling to AI/crypto narratives.
  
  

3) Math & inference

• Belief propagation: treat SSTK price -20% as evidence

• PCMCI result: downstream AI meme coin liquidity (e.g., GOAT / TURBO) volume predicts +40%

• Metric: MCI tests are extremely high → suggests direct causality, not confounded co-movement (e.g., broad market drawdown)
  
  

4) Example UI output

Risk warning

This isn’t just procurement. It’s a Titanic ticket.

Causal link

SSTK (legacy media) — (negative, τ = 24h, R² = 0.78) → AI Agents & Crypto (NEAR / WLD)

Abel inference

SSTK no longer tracks “ad spend.” It now inversely tracks the AI Displacement Index.

• Sharp: Markets are shorting human creative labor via SSTK. When AI concept names pump (NVDA/MSFT) or AI tokens rally (NEAR), SSTK implied vol spikes.

• Certain: Each iteration in generative capability shifts SSTK’s valuation center down ~15% (model-implied).

• Fun fact: Your annual subscription is effectively subsidizing a company being algorithmically eaten alive.
  
  

Decision tip

Shorten contracts to 3 months. Use savings to subscribe to Midjourney—or own AI infra exposure. If you can’t beat the predator, join it.

Case Study 3: Cross-Border Consumption and the FX/Culture Butterfly Effect

Theme: Travel + luxury shopping + macro arbitrage

User question

“I’m going to Japan and want to buy LV/Chanel—good timing?”

1) Entry tickers (T₀)

• JPY=X (JPY FX), LVMUY (LVMH ADR) / MC.PA, 9201.T (ANA)
  
  

2) Propagation + Surprise

• Obvious chain: weaker JPY → cheaper trip

• Abel causal discovery:
  
  

  • JPY depreciation → (τ = 3 months) → MC.PA revenue surprises

  • JPY → 3086.T (J. Front Retailing: department stores)

  • Surprise link: JPY depreciation → (negative) → BTC (fiat debasement hedge narrative)

  • Deep surprise: JPY impacts secondhand luxury prices (e.g., REAL / WOSG) via global resale arbitrage: cheap Japan sourcing increases global supply, compressing resale prices.
    
    

3) Math & counterfactuals

• Counterfactual: “If JPY hadn’t depreciated 10% over the last 3 months, what happens to LVMH earnings?”

• Result: 9201.T load factor has very high lag fit with JPY (R² > 0.8), but LVMH sensitivity to JPY shows structural break recently—brands began global repricing (brand-led intervention).
  
  

4) Example UI output

Macro boomerang
You think you’re arbitraging. The market is arbitraging your assets.

Causal link

JPY weakness → Japan retail → (global spillover) → luxury resale price compression

Abel inference:

This is a classic boomerang causal chain.

• Sharp: Your “cheap” is an FX illusion. Every -5% JPY move leads to ~-3% average global resale prices one month later (model estimate).

• Certain: Why? Global buyers sweep Japan, then dump supply internationally. Supply shock dilutes resale value.

• Fun fact: Buying luxury in Japan during a flood is like catching rainwater—catching more lowers the water level (global resale).

Decision tip

If it’s pure consumption joy, go for it—JPY still helps. If you care about investment-like resale, the model suggests owning the “toll booth” (Japan retail / trading houses) rather than doing the haul yourself.

World Causal Simulator

Given our data and graph, we can extend toward a broader World Causal Simulation problem.

These examples highlight what makes Abel different from other AI & LLM products:

1. It doesn’t just agree with the user. It sees a deeper game via the causal topology.

2. It turns any behavior into an “investment.” Everything can be a ticker; everything has a series, price, volume.

3. Math is the story. We don’t tell vague narratives. We use time delay and causality (nodes, weights, models, do-calculus) to expose the hidden role of chance in life and markets.
   
   

The experience should make users feel:

This AI understands the money-and-people math behind the world—sometimes better than I do.

And this is just the beginning. We can expand into deeper decision domains. Here are three canonical Life Strategy scenarios.

Scenario 4: Market Timing for Career

User question

“Should I switch jobs right now?”

Mapping

• LLM layer: “job switching” → labor-market liquidity + risk-asset pricing

• T₀ anchors: JOLTS, proxy for LinkedIn via MSFT productivity, HYG (high-yield credit as corporate expansion appetite)
  
  

Discovery & logic

• Core chain: HYG (credit spreads) → (τ = 6 weeks) → VC funding / IPO proxy (IPO) → (τ = 1 month) → tech hiring freeze

• Surprise nodes: FVRR / UPWK

• Insight: HYG (financing cost) is a strong parent of JOLTS. When spreads widen, companies cut headcount budgets first.
  
  

The math

• IRF: do(Credit_Spread ↑ 100 bps) → response on “Tech Layoff Index”

• Result: HYG is at a stress zone; IPO activity predicted -40% in 3 months → “good seats” disappear fast.
  
  

UI output

Abel Insight Card: Career Weather Station

Signal

Red Flag

“Don’t move. Switching ships mid-storm is how you drown.”

Causal link

HYG → (leads ~6w) → startup runway (how long your new company survives) → hiring quality

• Sharp: Fewer interviews isn’t your résumé. It’s financing cost spiking.

• Certain: In credit tightening cycles, wage premium compresses ~15% and “last in, first out” layoff risk triples.

• Fun fact: Recruiter silence isn’t lack of demand—CFOs parked the budget in Treasuries (US10Y).
  
  

Decision tip

Hold. Best strategy is “stay internally, watch externally.” When HYG rebounds (credit loosening), that week is your best swing.

Scenario 5: Real Estate as a Bond Proxy

User question

“Should I buy a home now?”

Mapping

• LLM: buying a home = short liquidity + long building materials + leveraged rate bet

• T₀ anchors: ITB, MBB, LUM (lumber), VNQ
  
  

Discovery & logic

• Core chain: US10Y (inverse, τ=0) → MBB → (τ = 2 weeks) → mortgage rates → (τ = 6 months) → home prices

• Surprise nodes: OPEN / Z volatility

• Insight: LUM crashes often lead home price declines by 3–5 months (cost-side collapse). MBB action signals bond market pessimism.
  
  

The math

• Confound checks: seasonality removal

• Conditional independence: given US10Y spike, ITB decouples from CPI and becomes rate-dominated

• Prediction: fair value implies ~-15% home price adjustment potential (not yet realized)
  
  

UI output

Abel Insight Card: Asset Lightning Rod

Signal

Catching a Falling Knife

“Homes are to live in. But today’s pricing is built to kill you.”

Causal link

US10Y → kills MBB → (lag ~6mo) → your house price

• Sharp: Ignore realtors—watch bond traders. Smart money is exiting MBB.

• Certain: Lumber leads housing by ~4 months. Lumber already halved → cost support vanished.

• Fun fact: Half your payment is paying for past Fed hikes, not for bricks.
  
  

Decision tip

Rent. Hedge via EQR or TLT. Buy only after MBB bottoms and LUM stabilizes.

Scenario 6: Human Capital Moats vs. AI Displacement

User question

“Should I go all-in on learning AI?”

Mapping

• LLM: learning AI = long compute demand vs short legacy outsourcing

• T₀ anchors: NVDA, CHGG, FVRR, MSFT
  
  

Discovery & logic

• Core chain: NVDA revenue → SMCI/DELL (servers) → (negative) → FVRR/UPWK gigs

• Surprise node: CHGG

• Insight: brutal substitution effect. When capability rises, legacy education and low-end freelance labor devalue fast.
  
  

The math

• Scenario analysis: do(AI_Capability ↑)

• Outcome: low-skill labor value trends toward zero; high-skill architecture/infrastructure value rises exponentially

• R² / beta: infra spend can have negative beta to “average developer demand” (polarization)
  
  

UI output

Abel Insight Card: Evolve or Die

Signal

Strong Buy on Yourself

“Don’t just ‘learn’ AI. Become the driver—or you become fuel.”

Causal link

NVDA (compute) destroys CHGG & FVRR, boosts AI architects

• Sharp: NVDA’s chart is the gravestone of legacy skills. If a skill can be tokenized, it’s depreciating at speed.

• Certain: Markets are selling “people who only write code” and buying “people who use AI to solve real problems.”

• Fun fact: AI salary premiums are the scarcity pricing of humans who can steer a compute monster.
  
  

Decision tip

All-in—but correctly. Don’t grind CRUD. Learn CUDA, agentic workflows, causal inference, deployment architecture. Bind your career to NVDA’s uptrend, not FVRR’s down-channel.

Product + Engineering Direction

For AI Engineers

This is not “Graph RAG.” We’re not retrieving text—we’re retrieving the mathematical expression of global consensus. Causal edges discovered by Causal AI become truth grounding, filtering LLM hallucinations.

For Product Managers

Every user’s mundane life question (buy a computer, license images, travel shopping) is translated into the backend as assets, numbers, and games of flows. We’re not selling “answers,” we’re selling insight—the macro + micro quantified consequences behind decisions.

Most AI labs ship Ladder 1 answers. We’re building Ladder 2 & 3: deep causal reasoning, interventions, and counterfactuals—pushed to the extreme.

Output style should be: Sharp, Certain, Fun.

UI should follow an iceberg model:

• Above the surface: understandable “prophecies” + playful interactions

• Below the surface: hard causal math (CKG + inference) doing the real work
  
  

Product Strategy: Three Anchors

1. Decision Super-Entrance

   Every button follows the same loop:

   Conclusion → Why → Counterfactual → Action → Subscription Alerts
   
   

2. Subscription Alerts as the Retention Engine

   When key parent nodes shift from red → yellow → green and major events hit, push alerts. Users are lazy—let the system watch.
   
   

3. Minimal Explanation Set as the Moat

   Don’t show 100 edges. Show 3 strongest causal chains + 1 counterfactual every time.
   
   

The World as a Computable Causal Network

Treat the world as a computable causal network. Every stock and coin price, volume, and continuous statistic is a real-time sensor. Ask any life decision question, and we return a traceable answer via causal inference.

When users feel that:

• Buying a home isn’t about home prices—it’s about high-income job cycles + financing conditions

• Switching jobs isn’t about interviews—it’s about risk preference in the hiring market

• Learning AI isn’t about hype—it’s about hype splitting from real budgets

• Buying LV isn’t about “cheap”—it’s about FX arbitrage windows + the last stubbornness of middle-class purchasing power

• Buying a car for dating isn’t about horsepower—it’s about credit tightening + repricing of commercial trust
  
  

…that differential becomes a Sharp, Certain, Fun cognitive experience.

Every micro-decision becomes a mathematical projection onto global capital-market candles. No matter how media and LLMs hype it up, ticker price + volume is real money voting—the most honest projection of world data.

Using institutional-grade market data + causal algorithms to answer any decision question—especially personal decisions (and later, personalized ones)—is a dimensionality reduction strike against traditional consulting. Meanwhile, the user interaction loop and real-world data flywheel become the best training fuel to continuously strengthen the causal graph and evolve toward a world model.

Let’s make this Causal World Simulator happen.