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Causal AI in Asset Management

Asset managers face increased competition against a backdrop of highly uncertain market conditions. AI can create a decisive advantage. But correlation-based machine learning fails to inspire the trust that’s needed to influence critical decisions. Leading asset managers are leveraging Causal AI in asset management to gain a competitive edge.


Top of mind for our customers in asset management

  1. Elevated fee pressure
  2. Extreme macroeconomic uncertainty
  3. Optimizing portfolios from an ESG perspective
  4. Flawed machine learning technology

Causal AI solutions transforming asset management 

Average improvement in Sharpe ratios, compared to current “state of the art” machine learning:


  • Reliably generate signals at all frequencies
  • Enable transparent trading decisions and intuitive risk management, through increased explainability. 
  • Automate data science pipelines, freeing up quants to spend more time on hypothesis testing and less time on error-prone data munging. 
  • Explore billions of data points and huge optimization spaces in a fraction of the time of standard systems. 
  • Produce signals that are robust to regime shift.
  • Identify true relationships between investment factors and their causal drivers, to improve market positioning and timing. 
  • Make factor modelling more explainable while simultaneously improving performance. 
  • Construct alternative factors from vast external data lakes to augment existing signals.
  • Adapt factor models to integrate domain expertise.
  • Persistently diversify portfolios by identifying causal relationships between assets, that goes beyond correlations. 
  • Leverage a highly customizable portfolio construction framework that makes more explainable decisions. 
  • Enable the user to encode high-level constraints, assumptions and domain knowledge into the decision process.
  • Generate portfolios that are more robust to minor perturbations in the market and adapt far quicker to new macroeconomic conditions. 
  • Achieve lower volatility and more moderate drawdowns than traditional and machine learning-based portfolio construction methods.
  • Assess market impact, which is an inherently causal-intervention problem that can’t be solved with correlation-based machine learning. 
  • Produce robust predictions of volume, future liquidity, spread and other key targets. 
  • Identify orthogonal signals at the highest frequencies.
  • Develop strategies based on explainable causal models of price dynamics.
  • Track causal relationships between ESG pillars and financial performance. 
  • Recalibrate portfolio construction to optimize for ESG exposure and long-term growth without compromising expected returns.
  • Conduct stress testing for ESG risk via counterfactual analysis — a signature capability of Causal AI that allows the machine to investigate outcomes on which there is little or no historical data.
  • Optimize resource allocation of sales and marketing budget.
  • Deploy causal analysis to understand the factors that causally drive your clients’ actions
  • Incorporate domain expertise into causal models to supply business context and cost constraints.
  • Leverage external data and nowcasts to predict market disruptions and macroeconomic forces that influence clients’ behavior.

Learn more about causality-based portfolio optimization

With Causal AI enablement, asset managers can construct portfolios that adapt to shifting correlations between assets. Learn how Causal AI achieves 19% greater Sharpe compared to machine learning-based methods for allocation.

Trusted leader in the industry

How Causal AI Impacts Key Aspects of Asset Management

Systematic Trading

Causal AI examines entire dynamic systems to understand the true causal relationships that drive change – relationships that cannot be found with correlation-based techniques….

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Causal AI examines entire dynamic systems to understand the true causal relationships that drive change – relationships that cannot be found with correlation-based techniques. These new angles can be employed in signals research, execution and risk management to create investment opportunities that persist and are robust to unexpected regime changes.

causaLens combines up-to-date academic methods with proprietary R&D from a leading causality research lab. The platform fits easily into existing pipelines, allowing systematic investors to move ahead of existing approaches by applying causal techniques across the business.

Discretionary Asset-Management

Causal AI allows domain experts to include their own assumptions into the model, producing a new class of intelligence that learns from data, but within a contextualized view of the world. Discretionary managers can have the best of both worlds, where their expertise is augmented by state-of-the-art artificial intelligence, without relying on a black-box to get insights from data. These insights empower users to harness the full potential of their domain knowledge and become ‘quantamental’ managers.

See causaLens’ Economic Analysis and Risk Management solutions, designed for discretionary managers to use via our intuitive asset management toolkit. 

High Frequency Trading & Market Making

causaLens can be integrated directly into any pipeline to optimise high-frequency trading strategies, trade execution, and market making. Designed for high-throughput online time series prediction, our models continuously adapt in real time to discover the current causal drivers in the market and produce the most efficient execution.

Private Equity & Real Estate

Private equity investors often work in low-data regimes in which standard machine learning techniques are prone to overfit, because they confuse correlation with causation. But data-driven approaches can produce outperformance when used properly. causaLens produces robust, explainable insights and actionable forecasts from limited data, and our causalNet technology allows domain experts to embed their knowledge directly into the AI.

Data Search & Building New Products

causaLens empowers exchanges, data sourcing teams & data vendors to autonomously discover value in time-series data. Standard AI techniques are confused by large amounts of intercorrelated and mismatched data; only Causal AI allows users to extract valuable signals and build exciting data products that are easily monetized.