Name: Fuelling a Smarter Pricing Strategy with Causal AI
Start: 2023-01-27T17:15:00Z
End: 2023-01-27T17:15:47.000Z
Location: BrightTALK
Rating: 5

causaLens is the pioneer of Causal AI—a giant leap in machine intelligence.

Today’s machine learning algorithms extract correlations from data and predict outcomes based on patterns in past data. Correlations are useful for making predictions, but they’re of little use for decisions. Causal AI goes beyond predictions by understanding the actual causes behind an outcome and quantifying the impact of different interventions. It is the only form of machine intelligence that can answer “Why?”.

causaLens builds Causal AI-powered products that empower all users to make superior decisions and drive business value. Leading organisations across a wide range of industries trust causaLens with their most important decisions.

The webinar will introduce how Causal AI can help optimize manufacturing processes, featuring Root Cause Analysis that can deliver benefits of reduced downtime, reduced defects, as well as increased efficiency & throughout. It will feature a live demo of a manufacturing decisionApp, and will answer questions at the end.

Manufacturing Optimization with Causal AI

decisionOS is an operating system that transforms raw data into trusted decision making workflows and is powered by Causal AI, the only technology that can reason like humans do.

decisionOS enables data science teams to build powerful Causal AI models and harness their intelligence for decision-making. They can also leverage a powerful application development framework to deliver decision-making applications to business stakeholders. 

decisionOS provides capabilities to operationalize decision workflows and integrate to other enterprise systems.

In this webinar we will present:
- Why an OS for decision-making
- How it works
- Current real-world applications

decisionOS: The First Operating System for Decision-Making

The Causal AI Conference
Welcome to the Causal AI Community: Scaling the Global Adoption of Trustworthy AI
Darko Matovski
CEO, Co-Founder, causaLens

This talk celebrates the establishment and rapid growth of the Causal AI community and introduces The Causal AI Conference as part of a more excellent vision to nurture and support the community.

Darko will discuss the emergence of Causal AI, and the conditions that have come together to create the right environment for Causal AI to thrive. How these have led to the Causal AI revolution, with exploding interest from academia, start-ups, investors and big tech alike.

He will share his experience with adopting Causal AI throughout different sectors, introduce the current state of the art of Causal AI in practical applications, and present his vision for the future role of Causal AI in our community and society.

Learn more and join the community here:
https://www.causalaiconference.com/

Welcome to the Causal AI Community

The Causal AI Conference 2022:
Actual Causality: A Survey
Joe Halpern
Professor - Computer Science, Cornell University

What does it mean that event C “actually caused” event E?

The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. What exactly was the actual cause of the car accident or the medical problem?

The philosophy literature has been struggling with the problem of defining causality since the days of Hume, in the 1700s. Many of the definitions have been couched in terms of counterfactuals. C is a cause of E if, had C not happened, then E would not have happened.

In 2001, Judea Pearl and I introduced a new definition of the actual cause, using Pearl’s notion of structural equations to model counterfactuals. The definition has been revised twice since then, extended to deal with notions like “responsibility” and “blame”, and applied in databases and program verification.

In this talk, Joe will survey the last 15 years of work, including collaborations with Judea Pearl, Hana Chockler, and Chris Hitchcock.

Learn more and join the community here:
https://www.causalaiconference.com/

00:00 - Welcome
00:15 - Actual Causality: A Survey
44:35 - Q&A

Actual Causality: A Survey

The Causal AI Conference 2022: 
Establishing Partnerships for the Learning of Causal Relationships in Medicine
Nicholas Chia
Associate Professor, Mayo Clinic

The talk will introduce the problems with ‘vanilla AI’ (non-causal AI), providing some thought-provoking examples. Nick will then discuss the implications for ethics and fairness and present his cutting-edge work on “The Unreasonable Effectiveness of Inverse Reinforcement Learning in Advancing Cancer Research”.

“Can we use causal inference methods to understand the molecular basis of cancer?” 

Nick will deliberate on this important question and attempt to answer it through interesting examples.

Several of the applications of Causality to medicine and healthcare will be presented, including exciting work carried out with NASA.

Learn more and join the community here:
https://www.causalaiconference.com/

00:00 - Welcome
01:05 - Establishing Partnerships for the Learning of Causal Relationships in Medicine
23:40 - Q&A

Establishing Partnerships for the Learning of Causal Relationships in Medicine

The Causal AI Conference 2022: 
Causal AI in the Energy Industry - Lessons learned at TotalEnergies
Antoine Bertoncello
Head of Next Generation AI, TotalEnergies

One increasingly popular approach is to use machine learning algorithms to predict the future behaviour of a system (e.g., failure of equipment, the energy produced by renewable assets…) based on correlations found in the data. However, a prediction is sometimes insufficient, and one may want to know what will happen if a specific variable is changed to minimize the failure rate or optimize production. This step is particularly difficult because it requires going beyond correlations and instead inferring a causal relationship between variable A and outcome B. The concept of causality is, in general, answered through experiments, such as randomized control trials, experimental design, and simulation. However, in industrial settings, experiments are not feasible, and engineers and data scientists often only have access to observational data on the impacts on productivity.

Epidemiologists, economists, and computer scientists have recently developed a range of statistical tools to go beyond correlation to discover and infer causal relationships. However, their use remains scarce in the energy world. In the last three years, TotalEnergies launched a research project to assess the use of causal inference in the company. In this talk, Antoine will describe a selection of the business cases that were identified, the approaches used, as well as the new ones we are developing with their academic partners.

Learn more and join the community here:
https://www.causalaiconference.com/

00:00 - Welcome
00:50 - Causal AI in the Energy Industry: Lessons learned at TotalEnergies
23:50 - Q&A

Causal AI in the Energy Industry: Lessons learned at TotalEnergies

Causal AI Conference 2022: 
Understanding Sales Drivers - Causal AI at Nestlé
Jordi Mur
Analyst, Emerging Technologies Nestlé Innovation, Nestlé

The food sector is highly complex and competitive. In such a data-rich environment, Nestle IT Innovation supports the development and adoption of Next AI B2B tools across the business, from edge AI to causal inference, to quantum computing, to name a few.

In this talk, Jordi will illustrate use cases and learnings from our experience bringing causal AI to Nestlé.

Learn more and join the community here:
https://www.causalaiconference.com/

00:00 - Welcome
00:46 - Understanding Sales Drivers: Causal AI at Nestlé
22:27 - Q&A

Understanding Sales Drivers - Causal AI at Nestlé

The Causal AI Conference 2022: 
Modelling and Decision-Making in an Ever-Changing World
Annie Hou
SVP - Global Head of AI & Behavioural Sciences, MRM

Over 2500 years ago Heraclitus said that “the only constant in life is change”. While this is now a clichéd quote painted on walls (and used in conference abstracts), these words could not reflect reality more so than in our current times. The frequency of black swan events has risen dramatically over the past few decades. Yet, one of the core assumptions of many ML models is that future events can be captured in historical data. Coupled with the explosion of big data and data-driven processes, many decision-makers have become accustomed to ‘predictive models’ that provide singular solutions in spite of a constantly changing world.

In this talk, Annie will discuss the need for models that allow for testing interventions and counterfactuals (the 2nd and 3rd rungs on Judea Pearl’s Ladder of Causality) in decision-making. She will share her experience building these decision-support tools:

- Building tools for data science and decision-makers with innovative technologies
- Getting clients to use these tools and how they respond
- Understanding the potential value of adopting Causal AI for marketing

Learn more and join the community here:
https://www.causalaiconference.com/

00:00 - Welcome
00:48 - Modelling and Decision-Making in an Ever-Changing World
27:59 - Q&A

Modelling and Decision-Making in an Ever-Changing World

The Causal AI Conference 2022:
An Emerging Solution to Harmonize Various Causal Discovery Methods
Nima Safaei
Senior Data Scientist, Scotia Bank

Explainability is one of the most desired properties of AI systems; without which the AI systems cannot be trusted in high-risk fields. Causal Inference (CI) is a vital tool for producing more insightful explainability. However, one major shortfall in the current CI literature is the lack of a unique definition for causality; resulting in many different methods such as pairwise dependency tests, statistical conditional tests, structural models, and graph-based models. One major barrier to the use of CI for explainability in AI applications is that the various CI methods usually result in different causal graphs with different inter-connectedness and density; specifically given a high-dimension feature space.

In this talk, Nima will address this challenge from the perspective of the financial services industry, walk through the associated complexities, and outline the possible methods to find a solution that harmonizes various CI methods.

Learn more and join the community here:
https://www.causalaiconference.com/

00:00 - Welcome
00:16 - An Emerging Solution to Harmonize Various Causal Discovery Methods
25:23 - Q&A

An Emerging Solution to Harmonize Various Causal Discovery Methods

The Causal AI Conference 22
Panel Discussion - Why there’s a lack of trust in AI and how can we fix it?

Speakers:
Puneet Gupta, Andre Franca, Nicholas Chia & Stephen Pritchard

Learn more and join the community here:
https://www.causalaiconference.com/

00:00 - Welcome
01:37 - Panel Introductions
08:17 - Why there’s a lack of trust in AI and how can we fix it?
38:02 - Q&A

Panel Discussion - Why there’s a lack of trust in AI and how can we fix it?

The Causal AI Conference 2022: 
The Causal AI Community Today and Beyond
Max Sipos
CTO, Co-Founder, causaLens

Conventional AI approaches, such as generative language models, generate data based on correlations. Causal AI instead, attempts to discover the accurate data-generating process which underpins the mechanism of cause and effect in the world. This is necessary for the accurate application of AI via scenario modelling and interventional and counterfactual reasoning in complex systems, such as modern economies. Deploying Causal AI in the enterprise is fraught with difficulties that the community has started to address today. In this talk, we will first discuss the necessary interfaces that permit Causal AI systems to cooperate in the enterprise. We will also discuss how to put Causal AI into the hands of the business user in the most efficient way possible, through developing suitable user interfaces and advances in explainable and robust AI. Finally, we will discuss the future challenges ahead of us, and where Causal AI will fit in the rapidly evolving AI landscape.

The Causal AI Community Today and Beyond

Prediction is at the core of running an efficient supply chain. Whether it’s forecasting demand or the weather; prediction enables our vastly complex global supply chains to operate in real-time and deliver on customer needs. However, the current approach to machine learning relies on past patterns and correlations to make predictions about the future – which makes it prone to fail in a constantly changing world.

This talk introduces Causal AI and its impact on identifying the Root Cause of delays throughout the entire supply chain, providing recommendations to mitigate delays and risk, and empowering supply chain, operations, and manufacturing leaders to make decisions that will improve customer service while reducing costs.

After watching this session, you’ll be able to:

Explain how Causal AI will improve AI explainability, trust, and human interaction
Understand how Causal AI overcomes the limitations of traditional AI and why over 85% of current AI projects fail.
Leverage Causal AI to vastly improve capacity utilization, On-Time & In-Full (OTIF) service levels while reducing safety stock
Explain how causaLens decisionApps can help teams run what-if scenarios at scale and use counterfactuals to re-imagine your supply chain

Supply Chain Root Cause Analysis with Causal AI

CMOs that boost marketing attribution can achieve transformational results: they are 2x more likely to achieve double-digit sales growth and can realize cost efficiencies of 25%.

Are you thinking about or asking your teams questions like:
- How should I allocate my marketing budget when customer behaviour and preferences are radically shifting?
- How can I achieve greater marketing impact with a reduced budget?


You aren’t alone and the answers will require a better understanding of the world that your customers are living in, noisy, highly interconnected, and constantly changing.

The world’s leading marketing teams are moving away from traditional AI and turning to Causal AI to unlock our understanding of each step along the customer journey. causaLens has pioneered this new category of intelligent machines that understand cause and effect.

Join this webinar to learn:
- How Causal AI enables attribution models to more accurately represent today’s complex world.
- How Causal AI can deliver on the promise of precision recommendations to budget allocation, at scale.
- From a demo of causaLens’ Marketing Attribution Decision App how marketers and teams can assess causal drivers and run experiments at scale.

Reimaging Marketing Attribution With Trustworthy AI

Only 17% of marketers use AI today, despite AI being touted as the ultimate marketing optimizer for the past decade. What is stopping us from harnessing this technology to better understand our customers? Can AI truly give us insights that help us attract and retain customers and increase ROI? The world’s leading marketing teams are moving away from traditional AI and turning to Causal AI to unlock their understanding of each step along the customer journey.

Hear from causaLens’ Andre Franca, Director of Applied Data Science, and Zubair Magrey, GM Marketing, as they explain how their team has pioneered this new category of intelligent machines that understand cause and effect.

After this session, you’ll be able to:
- Understand how Causal AI overcomes the limitations of traditional AI for marketing
- Leverage Causal AI to understand not just what action a customer may take, but also why they behaved the way they did
- Rely on real-world examples of how Causal AI maximizes customer retention and budget allocation optimization
- Use Causal AI to deliver on the promise of precision recommendations to budget allocation, at scale
- Explain how causaLens’ Decision Apps can help teams run experiments at scale to probe which channels and messages are driving KPIs

How Causal AI is Empowering Advancements in Marketing

Watch our Human-Guided Causal Discovery webinar, where we unveil our unique human-machine approach where domain experts & scientists/engineers collaborate to discover causal graphs.

Current AI approaches cannot be trusted for decision-making in the enterprise. They rely on statistical correlations that are often spurious and can’t be explained.

The causaLens Causal AI platform delivers unparalleled explainability & trustworthiness and is the only approach that works for AI-powered decision making. Discovering causal diagrams is the first & fundamental step to achieving this.

The talk covers:
- Why causal graphs are key to building Trustworthy AI
- An overview of the existing methods for Causal Discovery and their limitations
- A live demo of our Human-Guided Causal Discovery

Human Guided Causal Discovery

Stop trying to predict churn. Start by understanding why customers stay and then find more of them.

Are you still trying to prevent churn? It’s time to reframe how we are thinking about the churn problem. We suggest starting with: Who are the customers that are more likely to stay, and how can we find or foster more of them?

While conventional AI systems can predict likely churners, they don’t recommend a course of action to prevent churn. Causal AI identifies the true drivers of churn: it is uniquely capable of recommending a set of interventions to optimally allocate resources and budgets to increase retention, based on business goals and key metrics.

Watch this webinar to learn:
- How we can identify those customers most likely to stay, and more importantly explain why.
- How Causal AI can deliver on the promise of precision recommendations that drive the desired behavior.
- How Causal AI can provide reactive interventions based on who the customer is and what is most likely to retain them.

Maximizing Customer Retention with Causal AI

This talk covers:
- Problems with ML – what got us here won’t get us there
- Regulatory convergence and the way forward
- Measures of Explainability, Trust, Fairness and Bias
- Causal AI as a 10X accelerator in Financial Services
- Examples of Causal AI in Fraud Detection, Credit Risk and Stress Testing


Time stamps:
0:33 - What do we do?
3:33 - Challenges with current ML approaches
6:55 - Intro to Causal AI
10:42 - Example: Causal AI for Fraud/Crime Prevention
20:32 - Example: Causal AI for Credit Risk
26:06 - Example: Causal AI for Stress Testing
33:54 - Summary and closing remarks

The Future of AI in Banking

Organizational investment in pricing and promotions continues to grow, yet 70% of promotions are delivering a negative return. In today’s ever-changing landscape, companies must enable a robust understanding of the causal drivers of business, including pricing and promotional changes.


Organizations are seeking to answer questions like the following:

What is the potential impact of changing macro, market, and competitive landscape?
How should I change my seasonal strategy, win the season with a more informed approach?
How can I balance growth with profitability with predictable results?
What do I need to do to inform smoother inventory flow, in and out of promotional windows?
Now is the time when organizations are turning to causal ai to inform an understanding of the causal drivers of their business enabling a clear picture of the impact of the pricing and promotion execution.

After this talk you will be able to answer: 

How does Causal AI enable models to deliver more transparent, trusted and explainable insights?
How can teams leverage Causal AI models to inform smarter scenario planning and counterfactual analysis?
How does Causal AI enable a better understanding of other business drivers to support a more complete and integrated plan?

Fuelling a Smarter Pricing Strategy with Causal AI

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Fuelling a Smarter Pricing Strategy with Causal AI

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