Causal Models vs Predictive Models in Data Science Applications

Introduction

Data science has emerged as a vast field, and models are used for everything from making predictions to understanding critical cause & effect relationships.

This blog explores these models, pointing out key applications to help readers choose the right approach for their problem.

Predictive Models

The prediction models are built to foresee future outcomes or unknown values based on patterns observed within historical data. They are concerned with finding statistical associations in the data and leveraging them to make accurate predictions. These models do not concern themselves with understanding the mechanisms or causes behind the patterns. They aim to predict the most likely outcome.

Characteristics of Predictive Models

Objective: The primary goal of predictive models is to estimate or forecast an outcome with high accuracy. For instance, a predictive model might forecast next month’s sales figures based on historical trends.

1. Data Dependence: Predictive models rely entirely on historical data and the relationships that exist within it. These relationships may be correlations, and the model does not necessarily distinguish between correlation and causation if the prediction is accurate.
2. Focus: Predictive models focus on identifying patterns and trends. They are not designed to explain why a particular outcome occurs, only to estimate the likely outcome.
3. Evaluation: The performance of a predictive model is judged based on its predictive accuracy. Common evaluation metrics include measures like accuracy, precision, recall, F1-score, or mean squared error, depending on the type of problem (classification or regression).

Causal Models

Causal models, on the other hand, search for cause-and-effect relationship identification and measurement. From predictability to explaining why something happens and how it can be controlled, causal models are also about influence. Indeed, assessment of effects resulting from intervention or change of interventions often relies on the causality models rather than correlation.

Characteristics of Causal Models

Objective: The primary aim of causal models is to identify whether a change in one variable directly causes a change in another. For example, does a new drug reduce blood pressure, or does a marketing campaign increase sales?

1. Data Dependence: While data plays a crucial role, causal models require domain knowledge and carefully controlled assumptions to isolate causal relationships. Confounding variables and factors influencing the cause and effect must be accounted for to avoid misleading conclusions.
2. Focus: The emphasis is on understanding mechanisms. Causal models explore how an intervention (e.g., a price discount) will affect an outcome (e.g., sales) and seek to separate true causal effects from mere correlations.
3. Evaluation: Causal models are evaluated based on their ability to estimate the effect of interventions or changes accurately. Success is measured by the validity of causal assumptions and the robustness of the findings rather than predictive accuracy.

How Predictive Models and Causal Models Differ?

Predictive and causal models play different roles, even if based on the same data sets. Predictive models involve forecasting future or unknown outputs based on correlation and statistical association in the data set. They target accuracy on what will probably happen, as measured with metrics such as RMSE (Root Mean Square Error) or classification accuracy. They do not relate, however, to the causes of the patterns observed.

In causal models, the goal is to isolate cause-and-effect relationships among variables, often accounting for confounders through randomization or instrumental variables. Their purpose is to understand why an outcome happens, and success in terms of causal estimates depends on their validity and robustness rather than predictive accuracy.

Applications

Predictive models are advised for tasks including demand forecasting, fraud detection, and customer segmentation. For instance, a retailer would decide to use predictive models by making a guess about the units of some product he sells over the holidays.

These are causal models applied to interventions, determining whether a new drug helps shorten the patient’s recovery or if redesigning a website could result in a higher conversion rate.

Techniques Used in Each Model

Techniques in Predictive Models

Predictive models typically rely on machine learning algorithms and statistical techniques for accuracy. Common methods include:

• Linear Regression: For predicting continuous variables.
• Random Forests and Gradient Boosting: This is used to handle non-linear relationships in data.
• Time Series Models (e.g., ARIMA, LSTMs): For forecasting based on temporal patterns.

Techniques in Causal Models

Causal models require specialized methodologies to infer causation:

• Randomized Controlled Trials (RCTs): The gold standard for causal inference, where subjects are randomly assigned to treatment and control groups.
• Difference-in-Differences (DiD): Compares changes in outcomes between treated and untreated groups over time.
• Instrumental Variables (IV): Used when confounding variables cannot be directly controlled.
• Directed Acyclic Graphs (DAGs): Visual tools that map out assumptions about causal relationships.

How to Decide on Predictive & Causal Models?

Predictive models are ideal for estimating future outcomes or identifying patterns, such as forecasting sales, detecting fraud, or recommending products. In contrast, causal models are used to understand the impact of actions or policies, making them essential for evaluating public policies, measuring the effectiveness of treatments, or assessing the ROI of marketing campaigns.

Why Understanding the Difference Matters?

Choosing between predictive and causal models is key to making well-informed decisions. Predictive models can give us accurate forecasts, relying solely on predictions without understanding the underlying causes, which can lead to unexpected results. Causal models help us understand why things happen, offering valuable insights into what works and why so businesses and policymakers can make smarter, more effective choices.

Drop a query if you have any questions regarding Causal or Predictive models and we will get back to you quickly.

About CloudThat

CloudThat is a leading provider of Cloud Training and Consulting services with a global presence in India, the USA, Asia, Europe, and Africa. Specializing in AWS, Microsoft Azure, GCP, VMware, Databricks, and more, the company serves mid-market and enterprise clients, offering comprehensive expertise in Cloud Migration, Data Platforms, DevOps, IoT, AI/ML, and more.

CloudThat is the first Indian Company to win the prestigious Microsoft Partner 2024 Award and is recognized as a top-tier partner with AWS and Microsoft, including the prestigious ‘Think Big’ partner award from AWS and the Microsoft Superstars FY 2023 award in Asia & India. Having trained 650k+ professionals in 500+ cloud certifications and completed 300+ consulting projects globally, CloudThat is an official AWS Advanced Consulting Partner, Microsoft Gold Partner, AWS Training Partner, AWS Migration Partner, AWS Data and Analytics Partner, AWS DevOps Competency Partner, AWS GenAI Competency Partner, Amazon QuickSight Service Delivery Partner, Amazon EKS Service Delivery Partner,  AWS Microsoft Workload Partners, Amazon EC2 Service Delivery Partner, Amazon ECS Service Delivery Partner, AWS Glue Service Delivery Partner, Amazon Redshift Service Delivery Partner, AWS Control Tower Service Delivery Partner, AWS WAF Service Delivery Partner, Amazon CloudFront and many more.

To get started, go through our Consultancy page and Managed Services Package, CloudThat’s offerings.