To Build Truly Intelligent Machines, Teach Them Cause and Effect.
Correlation is not Causation!.
This post is the first of the series on Causal Machine Learning. I will start with the very basics of causal inference , Provide some basic background in Bayesian networks/graphical models , Show how graphical models can be used in causal inference and Describe application scenarios , case studies and the practical difficulties in Causal Inference. I will follow step by step approach and keep the things as simple as possible with lot of practical tutorials. In each of the session i will provide additional learning materials and references , which should help you to keep the flow. In this blog post , i will present Why we need causality in Machine Learning! . Enjoy the reading!
What is Causal AI ?
Deep learning techniques do a good job at building models by correlating data points. But many AI researchers believe that more work needs to be done to understand causation and not just correlation. The field causal deep learning -- useful in determining why something happened -- is still in its infancy, and it is much more difficult to automate than neural networks.
Even though we can observe correlation, it does not prove causation.
Correlation: measures the relationship between two things.
Causation: means that one thing will cause the other to happen.
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I know that when I see X, I will see Y (Association/Correlation) -
I know that X causes Y (Causality)
The distinctions between the two can have important implications. In the website, “Spurious Correlations” by Tyler Vigen, we can explore a wide variety of correlations that are due to chance. One of the interesting spurious correlation is listed below.
While these two variables have a correlation of 95.23%, it is highly unrealistic to think that a degree in mathematics caused an increase in the amount of Uranium stored in the United States!
“If Correlation Doesn’t Imply Causation, Then What Does?”
We will uncover this in the upcoming blog posts.
The excitement in the field has been kindled by Judea Pearl, a professor at UCLA, who did some of the formative work on implementing Bayesian networks for statistical analysis. More recently he has been developing a framework for diagramming causation and teasing apart the factors that contribute to observed events in a computable framework.
In his latest book, “The Book of Why: The New Science of Cause and Effect,” he argues that artificial intelligence has been handicapped by an incomplete understanding of what intelligence really is. In his new book, Pearl, elaborates a vision for how truly intelligent machines would think. The key, he argues, is to replace reasoning by association with causal reasoning. Instead of the mere ability to correlate fever and malaria, machines need the capacity to reason that malaria causes fever. Pearl expects that causal reasoning could provide machines with human-level intelligence.
Understanding why requires understanding of the whats, the wheres, and the whens. The hows, however, seem to be an implementation of the whys !!
Traditional ML Vs Causal ML
Machine learning is a type of artificial intelligence that involves training algorithms to recognise patterns in data and make predictions or decisions based on those patterns. The goal of machine learning is to build models that can generalise to new data and make accurate predictions or decisions.
The main difference between machine learning and causal machine learning is the focus of the analysis. While traditional machine learning aims to predict an outcome based on patterns in the data, causal machine learning aims to identify the specific variables that are causing an outcome and how they contribute to it. This involves identifying and modelling the causal relationships between variables, rather than just the statistical relationships.
Causal machine learning is a type of machine learning that focuses on identifying the cause-and-effect relationships between variables. In other words, it aims to identify the factors that influence a particular outcome and how they influence it.
Causal machine learning can be used to improve the accuracy of predictive models by taking into account the underlying causes of the outcomes being predicted. It is also useful in a variety of applications, such as evaluating the effectiveness of interventions, predicting the impact of policy changes, and identifying the factors that contribute to certain outcomes.
Explainable AI vs Causal AI
Explainable AI (XAI) is a type of artificial intelligence (AI) that is designed to be transparent and interpretable, meaning that it can provide explanations for its decisions and actions. The goal of XAI is to make AI systems more transparent and understandable to humans, so that they can be trusted and used more effectively.
Causal AI, on the other hand, is a type of AI that focuses on identifying the cause-and-effect relationships between variables. It is a sub field of machine learning that aims to identify the specific factors that influence a particular outcome and how they contribute to it. One key difference between explainable AI and causal AI is their focus. Explainable AI is concerned with making AI systems more transparent and interpretable, while causal AI is focused on identifying the causes and effects of various phenomena.
Another difference is the techniques used. Explainable AI often employs techniques such as feature importance, sensitivity analysis, and model interpretation methods to provide insights into the decision-making process of AI systems. Causal AI, on the other hand, typically uses techniques such as structural equation modelling, instrumental variables, and counterfactual analysis to identify the causal relationships between variables.
While XAI and causal AI are related in that they both aim to increase our understanding of how AI systems work, they are distinct concepts. XAI focuses on providing explanations for the decisions and predictions made by AI systems, while causal AI focuses on identifying the underlying causes of outcomes.
One way in which XAI and causal AI can be related is that XAI methods, such as counterfactual analysis, can be used to identify the causes of particular outcomes or decisions. However, it is important to note that not all XAI methods are causal in nature, and not all causal AI systems are necessarily explainable. Overall, both XAI and causal AI are important tools for improving our understanding of how AI systems work and for increasing the transparency and trustworthiness of AI systems.
Applications
Causal machine learning can be applied in many different fields and industries to identify and understand the causal relationships between variables. Here are a few examples of the application of causal machine learning:
- Healthcare: Causal machine learning can be used to identify the factors that contribute to the development of certain diseases and to predict the likelihood of future events, such as hospital re-admissions. It can also be used to understand the factors that influence patient outcomes and to develop personalised treatment plans.
- Finance: Causal machine learning can be used to understand the factors that influence stock prices and to develop trading strategies. It can also be used to identify the causes of financial fraud and to develop strategies to prevent it.
- Marketing: Causal machine learning can be used to understand the factors that influence customer behaviour and to optimize marketing campaigns. It can also be used to identify the factors that drive customer loyalty and to develop strategies to retain customers.
- Education: Causal machine learning can be used to understand the factors that influence student learning and to develop personalized learning strategies. It can also be used to identify the causes of academic achievement gaps and to develop strategies to address them.
- Social science: Causal machine learning can be used to understand the factors that influence social outcomes and to develop policies that address social issues. It can also be used to identify the causes of social inequality and to develop strategies to address it.
These are just a few examples of the many ways in which causal machine learning can be applied. There are many more applications in various fields and industries.
Conclusion
In this post I have provided an introduction to Causal machine learning. I have tried to be as compact as possible. Below you can find some additional resources if you want to know more about Causal machine learning. Hope you enjoyed reading so far!.