Cause, effect, correlation – it’s easy to confuse one for another and make a wrong decision.
For instance, people buy more ice cream when sales of sun cream and parasols go up – so a tactical commercial decision might be to try to drive up the sales of parasols.
Or you place your energy on driving up the sales of ice cream, maybe a cheaper move that’s going to lead to the sale of the more expensive parasols.
All the while missing the fact that it’s the warm summer days that are causing the uplift in sales.
A simple example, perhaps, but it gets to the heart of why anomaly spotting needs to be an extremely precise exercise.
Anomalies and unusual patterns emerge frequently in the complex data analysis landscape, which raises concerns about our ability to predict trends accurately.
This is where the concept of causal analysis becomes applicable, a powerful approach that circumvents conventional methods and gives an even deeper insight into the complex chain of causes and effects in the data.
Data scientists work to identify the underlying causes of these anomalies to find much more than mere correlations of different variables.
Problems in Conventional Anomaly Detection Approaches
Statistical methods which identify correlations within data have long been used to detect anomalies. Though these methods have their strengths, often, they cannot reveal the intricate pattern of causes and effects underlying these anomalies.
However, they cannot uncover underlying causal factors. While these methods efficiently flag anomalies, they need to explain the ‘why‘ behind them; otherwise, these deficiencies hinder informed decision-making.
As we saw, a sudden increase in sun-shading parasol sales could see an increase in the purchase of ice cream under retail scenarios.
On the other hand, given that both relate to warm summer weather, a traditional approach may spot the trend but not provide any reason. This may result in incorrect assumptions and inferences based only on correlation.
A similar case can be observed in the energy sector, where increases in solar installations have coincided with a rise in sales of ice cream. This correlation can be detected, but the real cause might have been overlooked by conventional methods and left a significant gap in interpretation.
Thus, it hinders accurate decision-making by not being able to establish the precise cause of this inference.
What Is Causal Analysis?
Causal analysis in data science discovers cause-and-effect relationships between variables. The causal analysis looks deeper at how changes in one variable affect another, unlike simple correlation, which finds statistical links.
It provides evidence of the underlying mechanisms and factors that drive these changes. It is vital because it reveals actionable insight and goes beyond mere levels of connections to explain what’s happening.
How Does Causal Analysis Work?
Causal analysis systematically explores the connections between variables to ascertain if changes in one variable trigger changes in another.
In contrast to simple correlation, it takes a deeper look at causation by establishing a temporal sequence and addressing confounding factors.
Because correlation does not lead to causation — a strong statistical correlation between two variables does not necessarily mean that changes in one of the variables cause changes in the other.
This limitation is addressed in the causal analysis, which attempts to identify a causal link. It involves randomized controlled trials (RCTs), natural experiments, and statistical techniques like instrumental variable analysis.
The causal analysis considers factors such as the temporal order of the cause before the effect, a probable mechanism of how the cause might lead to the effect, and the absence of alternative explanations.
In particular, this aims to eliminate conflicting variables that cause a misleading correlation. The causal analysis gives a solid basis for determining why specific results are observed by examining these elements.
The significance of causal analysis is based on its ability to detect the root causes, not just surface connections.
It provides critical insights for developing decisions, policy formulation, and model refinement in different sectors.
This methodological approach allows organizations and researchers to make informed decisions and optimize models to increase their understanding of complex relationships between causes and effects.
How Does Causal Analysis Benefit Businesses?
The causal analysis benefits the businesses in the following ways:
Better Decision Making
The causal analysis provides insight into business choices by defining the root causes and giving a targeted strategy for expected results.
Effective Resource Utilization
Businesses can optimize their use of resources by finding impactful factors that prevent them from wasting resources merely based on correlation.
Accurate and Robust Models
Causal analysis improves machine learning and predictive modeling, refining their accuracy and robustness. It can improve the selection of features by identifying variables causally related to outcomes, which could also reveal data or model biases affecting forecasting effectiveness.
Causal analysis has a crucial role in policy development and strategic planning. Governments and organizations can formulate policies because of a thorough understanding of the causal relationship between various factors. This leads to more effective and focused interventions.
Approaches for Causal Understanding
Several techniques help understand the causal relationships among the variables in different scenarios. A few of such techniques are highlighted below:
Directed Acyclic Graph (DAG)
Directed Acyclic Graphs (DAGs) visually highlight complex causal relationships by representing variables as nodes connected by directed edges.
Deep causal understanding is developed by interventions within the DAGs involving variable change control to detect changes.
Practical applications include anomaly detection. DAGs expose hidden causes of anomalies in manufacturing, e.g., by identifying unintelligible variables that lead to irregularities.
Randomized Controlled Trials (RCTs)
This technique assigns subjects to different groups and allows researchers to estimate their effects on a particular variable. RCTs establish causal links in controlled experiments by controlling for potential confounders.
Using a regression model, which considers the effects of other variables, it is possible to measure the effect of one variable on an outcome.
This approach can allow us to see more clearly how a variable affects the outcome since we consider additional factors. For that reason, we can understand the relationship between variables across datasets with their connections to causes and effects through regression analysis.
Challenges and Ethical Considerations
- The potential of causal analysis is evident, but it has certain practical issues. For example, focusing on data quality, methodology selection, and technical resources is essential to implement causal analysis.
- It is also challenging to interpret causal analysis results. Therefore, effective communication with different stakeholders is needed to translate complex causal relationships into concrete strategies.
- When applying causal analysis, ethical considerations are also important. There is a persistent need to ensure responsible use and transparency of decisions.To use a hyperbolic example, once you realize hot weather increases ice cream sales, you don’t figure out ways to speed up climate change.
The causal analysis goes beyond anomalies and provides clues as to the root cause, which is why precise decisions are made.
It surpasses the correlation by using methods such as DAG and RCTs to determine causation and allows companies to use the best resources, robust models, and informed policies.
Careful planning is required in terms of ethical considerations and implementation challenges. Causal analysis is crucial for effectively converting data to intelligence and guidance strategies.