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Cause, effect, correlation – it’s easy to confuse one with the other and make a wrong decision.
For example, people buy more ice cream when sunscreen and umbrella sales increase, so a tactical business decision might be to increase umbrella sales.
Or you can focus your energies on increasing sales of ice cream, which may be a cheaper move that will boost sales of the more expensive umbrellas.
However, you overlook the fact that it is the warm summer days that boost sales.
A simple example perhaps, but it sums up why anomaly detection must be an extremely precise task.Anomalies and unusual patterns are common in the complex landscape of data analysis, casting doubt on our ability to accurately predict trends.
This is where the concept of causal analysis comes in, a powerful approach that bypasses traditional methods and provides even deeper insight into the complex chain of cause and effect in the data.
Data scientists are working to uncover the underlying causes of the anomalies to find much more than just correlations between different variables.
Problems with conventional approaches to anomaly detection
Statistical methods that identify correlations within data have long been used to detect anomalies. While these methods have their strengths, they often fail to uncover the intricate pattern of cause and effect that underlies these anomalies.
Traditional methods such as z-score analysis and clustering are good at detecting anomalies based on statistical deviations.
However, they cannot uncover the underlying causal factors. While these methods are efficient in identifying anomalies, they must explain the “why” behind them, otherwise they hinder informed decision-making.
As we’ve seen, a sudden surge in umbrella sales could lead to a surge in retail ice cream purchases.
Since both are related to warm summer weather, a traditional approach might spot the trend but not provide a reason for it. This can lead to incorrect assumptions and conclusions based only on correlations.
A similar case can be seen in the energy sector, where the increase in solar installations has coincided with an increase in ice cream sales.
While this correlation can be identified, the root cause may have been missed using traditional methods, leaving a significant interpretation gap.
This hampers accurate decision-making because the exact cause of that conclusion cannot be determined.
What is causal analysis?
Causal analysis in data science uncovers cause-and-effect relationships between variables. In contrast to simple correlation, which finds statistical relationships, causal analysis looks at how changes in one variable affect another.
It provides evidence for the underlying mechanisms and factors driving these changes. It is critical as it provides actionable insights and goes beyond the mere connections level to explain what is happening.
How does causal analysis work?
Causal analysis systematically examines the connections between variables to determine whether changes in one variable trigger changes in another.
In contrast to simple correlation, it goes deeper into causal research by defining a chronological sequence and taking into account disruptive factors.
This is because correlation does not imply causation—a strong statistical correlation between two variables does not necessarily mean that changes in one variable cause changes in the other.
This limitation is taken into account in the causal analysis, which attempts to establish a causal relationship. It includes randomized controlled trials (RCTs), natural experimentation, and statistical techniques such as instrumental variable analysis.
Causal analysis considers factors such as the timing of cause and effect, a likely mechanism by which cause might lead to effect, and the lack of alternative explanations.
In particular, this is intended to exclude contradictory variables that cause a misleading correlation. Causal analysis provides a solid basis for determining why certain outcomes are observed by examining these elements.
The importance of causal analysis rests in its ability to identify root causes and not just superficial connections.
It provides important insights for the development of decisions, the formulation of strategies and the refinement of models in various areas.
This methodological approach enables organizations and researchers to make informed decisions and optimize models to improve their understanding of complex cause-effect relationships.
What are the benefits of causal analysis for companies?
Causal analysis brings the following advantages to companies:
Better Decision Making
Causal analysis provides insight into business decisions by defining root causes and providing a targeted strategy for expected outcomes.
Effective use of resources
Organizations can optimize their resource usage by finding powerful factors that prevent them from wasting resources solely on correlations.
Accurate and robust models
Causal analysis improves machine learning and predictive modeling by refining their accuracy and robustness.
It can improve feature selection by identifying variables that are causally related to outcomes, which can also uncover data or model biases that affect the effectiveness of forecasts.
Causal analysis plays a crucial role in policy development and strategic planning.
Governments and organizations can formulate policies when they clearly understand the causal relationships between different factors. This leads to more effective and targeted measures.
Approaches to causal understanding
Different techniques help to understand the causal relationships between the variables in different scenarios. Some of these techniques are presented below:
Directed Acyclic Chart (DAG)
Directed Acyclic Graphs (DAGs) represent complex causal relationships visually by representing variables as nodes connected by directed edges.
A deep causal understanding is developed through interventions within the DAGs that involve controlling the variable changes to detect changes.
Practical applications include anomaly detection. DAGs uncover hidden causes of anomalies in manufacturing, e.g. B. by identifying incomprehensible variables that lead to irregularities.
Randomized Controlled Trials (RCTs)
With this technique, subjects are assigned to different groups and researchers can estimate their impact on a given variable.
RCTs establish causal relationships in controlled experiments by controlling for possible confounders.
A regression model that accounts for the effects of other variables can be used to measure the effect of one variable on an outcome.
This approach makes it easier to see how a variable affects the outcome as we consider additional factors.
For this reason, with the help of regression analysis, we can understand the relationship between variables in different data sets and their connections to causes and effects.
Challenges and ethical considerations
- The potential of causal analysis is clear, but it also has some practical problems. For example, to perform causal analysis, it is imperative to focus on data quality, choice of methodology, and technical resources.
- The interpretation of the results of the causal analysis is also a challenge. Therefore, effective communication with different stakeholders is required to translate complex causal relationships into concrete strategies.
- Ethical considerations are also important when applying causal analysis. To use an exaggerated example, when you find that hot weather increases ice cream sales, you don’t think about how to accelerate climate change.
The bottom line
Causal analysis goes beyond anomalies and provides clues to the root cause, which is why precise decisions are made.
It goes beyond correlation by using methods such as DAG and RCTs to determine causality, enabling companies to employ the best resources, robust models and informed strategies.
Careful planning is required to address ethical considerations and implementation challenges. Causal analysis is critical to effectively transforming data into insights and guidance strategies.