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How can you learn causal inference methods for machine learning with uncertainty?

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Causal inference is the study of how to infer causal relationships from observational or experimental data. It is a crucial skill for machine learning practitioners who want to understand the impact of their models, interventions, or policies on real-world outcomes. However, causal inference is often challenging and uncertain, as it involves making assumptions, dealing with confounding factors, and handling missing or noisy data. In this article, you will learn some of the basic concepts and methods of causal inference for machine learning with uncertainty, and how to apply them to different scenarios and problems.

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1 Why causal inference?

Machine learning models are often trained to find patterns and correlations in data, but they cannot tell you why those patterns exist or how they would change under different conditions. For example, a machine learning model might predict that smoking is associated with lung cancer, but it cannot tell you if smoking causes lung cancer, or if there are other factors that influence both smoking and lung cancer. Causal inference can help you answer these questions by providing a framework and tools to test and estimate causal effects, such as the effect of smoking on lung cancer, or the effect of a new drug on patient survival.

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Ramit Sawhney

Global Head of Core AI & ML at Tower Research | RA at Georgia Tech & MBZUAI
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Causal inference is crucial because it allows us to move beyond mere correlation and identify the true drivers of phenomena. By establishing causal relationships, we gain insights into how changes in one variable can impact another, which is essential for making informed decisions in fields like healthcare, economics, and social sciences. This deeper understanding empowers us to design more effective interventions, policies, and strategies, ultimately leading to better outcomes and a more comprehensive grasp of the world around us.

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2 What is uncertainty?

Uncertainty is the degree of confidence or doubt that you have about your data, model, or inference. Uncertainty can arise from various sources, such as measurement errors, sampling variability, missing data, model misspecification, or unobserved confounders. Uncertainty can affect the quality and validity of your causal inference, as it can lead to biased or inaccurate estimates, or to false or misleading conclusions. Therefore, it is important to quantify and communicate uncertainty when doing causal inference for machine learning.

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Kewin Sachtleben

Head of Data Science @ DOJO - Smart Ways | Machine Learning | Data Science | AI Engineer | Generative AI | LLM
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Uncertainty is inherent in early scientific research, especially in complex fields like physics and data science. The relationship between alcohol consumption and impaired motor coordination exemplifies this. Initially, there was high uncertainty, with only hypotheses suggesting a negative impact of alcohol on motor skills. Through rigorous research, including controlled experiments and detailed analysis, this uncertainty was significantly reduced, confirming the causal link between alcohol consumption and decreased motor coordination. This process demonstrates the importance of methodical investigation in reducing uncertainty and advancing scientific knowledge.

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3 How to model causality?

One of the common ways to model causality is to use a graphical representation called a causal graph or a directed acyclic graph (DAG). A DAG is a set of nodes and arrows that show the causal relationships among variables. For example, a DAG can show that smoking causes lung cancer, and that age and gender are confounders that affect both smoking and lung cancer. A DAG can help you identify the causal effects of interest, the assumptions that you need to make, and the methods that you can use to estimate the causal effects.

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Kewin Sachtleben

Head of Data Science @ DOJO - Smart Ways | Machine Learning | Data Science | AI Engineer | Generative AI | LLM
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In modeling causality within data science, you could use: Shannon Entropy helps in assessing uncertainty in variables, pinpointing those with high unpredictability that may indicate causal factors. Mutual Information is useful for identifying dependencies between variables, essential in causal analysis. These tools aid in feature selection and are particularly valuable in time series analysis for understanding temporal causal relationships. Integrating these methods with techniques like Granger causality test can enhance causal modeling, yet it’s important to recognize each method's limitations (e.g. high entropy and dependency doesn't confirm causality, give an insight.) and suitability to specific data types and causal structures.

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4 How to estimate causal effects?

Estimating causal effects from data requires different methods depending on the type and quality of data, as well as the assumptions that can be made. Randomized controlled trials (RCTs) are considered the gold standard for causal inference, as they can eliminate confounding and ensure that the treatment is independent of the outcome; however, RCTs can be expensive, impractical, or unethical in many situations. Regression is a statistical technique which relates the outcome to the treatment and other covariates, allowing for adjustment of confounding and estimation of the average effect of the treatment on the outcome. Matching is a method where similar units are paired or grouped together to reduce confounding; however, it requires enough data to find good matches. Instrumental variables (IVs) are variables that affect the treatment but not the outcome, except through the treatment; this can help overcome endogeneity issues, but requires finding a valid and relevant IV.

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科尔曼大卫

人工智能顾问 - 云服务、机器学习、网络安全
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There are a number of ways to do it, observational methods are often overlooked and they rely on analyzing existing data to identify causal relationships. This can be done by looking for patterns in the data or using statistical techniques to control for confounding variables. Commonly used methods involve regression analysis, propensity score matching and difference-in-differences (DID) estimation and randomized controlled trials (RCTs).

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5 How to handle uncertainty?

There are various approaches to handle uncertainty when doing causal inference for machine learning, depending on the source and nature of uncertainty, and the goal and context of inference. Confidence intervals (CIs) can be used to assess the precision and reliability of an estimate, and capture its range of plausible values. However, CIs rely on assumptions and models that may not account for all sources of uncertainty. Sensitivity analysis can identify the sources and impacts of uncertainty, but may be computationally intensive, subjective, or incomplete. Alternatively, Bayesian inference combines prior beliefs and data to update posterior beliefs about a causal effect. It can incorporate different sources of information and uncertainty in a probabilistic way. Nevertheless, it requires specifying a prior distribution that can be subjective or controversial, as well as using computational methods such as MCMC that can be complex or slow.

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6 How to learn more?

If you want to learn more about causal inference methods for machine learning with uncertainty, there are many resources available online and offline. For instance, Miguel Hernan and James Robins' book 'Causal Inference: What If' covers the fundamentals and applications of causal inference, with examples and exercises in R and SAS. Additionally, Judea Pearl, Madelyn Glymour, and Nicholas Jewell's book 'Causal Inference in Statistics: A Primer' introduces the basic concepts and methods of causal inference, with illustrations and exercises in R and Python. Scott Cunningham also offers a course that teaches causal inference using econometric methods and data analysis in R. Lastly, Uber has developed a Python package called 'Causal ML', which implements various causal inference methods for machine learning such as meta-learners, doubly robust estimators, and uplift trees.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you learn causal inference methods for machine learning with uncertainty?

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3

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5

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1 Why causal inference?

2 What is uncertainty?

3 How to model causality?

4 How to estimate causal effects?

5 How to handle uncertainty?

6 How to learn more?

7 Here’s what else to consider

Machine Learning

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How can you learn causal inference methods for machine learning with uncertainty?

1

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3

4

5

6

7

1 Why causal inference?

2 What is uncertainty?

3 How to model causality?

4 How to estimate causal effects?

5 How to handle uncertainty?

6 How to learn more?

7 Here’s what else to consider

Machine Learning

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