Causal inference is the process of determining cause-and-effect relationships between variables, distinguishing between correlation and true causation. In marketing and business contexts, causal inference helps organizations understand which actions, campaigns, or strategies actually drive desired outcomes rather than simply being associated with them.
Judea Pearl introduced the hierarchical framework of Ladder of Causation that helps understand the different levels of causal reasoning and the questions they can answer. As a practical example, we can consider whether branded search advertising has causal relationship between sales or not. In the chart below, we have example data on branded search spend and sales.
For more on this topic, see What is Causality?
First level of causal reasoning focuses purely on observing the world and the data.
When analyzing marketing and sales data, it is often tempting to infer that branded search advertising directly drives sales due to their observed correlation. However, relying solely on this initial level of causal reasoning can lead to misguided decisions, as underlying confounding variables may not be properly accounted for.
At the second level, causal reasoning incorporates interventions such as controlled experiments and analytical models to directly assess causality.
For instance, a geo-experiment can be conducted by pausing branded search ads in selected regions while maintaining them in others, then applying causal inference methodologies to rigorously evaluate the impact of this intervention.
The third level of causal reasoning involves considering counterfactuals—assessing what would have occurred if a specific action had not been taken. This approach enables marketers to quantify the true impact of their decisions by comparing observed outcomes with hypothetical scenarios in which the intervention did not happen.
In the case of branded search advertising, this might mean modelling the outcome for users who did not click the branded search ad.
Causal graphs – often shown as Directed Acyclic Graphs (DAGs) – illustrate the relationships between variables in a system, with arrows showing presumed causal influence. By visualizing these connections, DAGs help marketers identify confounding factors, plan efficient experiments, and clarify how different variables impact results. Their use leads to more accurate attribution and more robust marketing analysis.
These are external factors that influence both the suspected cause and the observed effect, potentially creating misleading correlations. Effective causal inference methods account for confounding variables to isolate true causal relationships.
Causal inference often involves comparing groups that received different treatments (such as seeing an advertisement vs. not seeing it) to measure genuine impact.
The gold standard for establishing causation, RCTs randomly assign subjects to treatment and control groups. In marketing, this might involve A/B testing different ad creatives or randomly selecting markets for campaign rollouts.
When randomization is not possible, marketers can leverage naturally occurring variations. For example, analyzing sales performance across regions with different media coverage due to geographic constraints.
This technique uses variables that affect the treatment but only influence the outcome through the treatment itself. Media weight variations due to inventory availability can serve as instrumental variables for measuring advertising effectiveness.
This approach exploits arbitrary cutoff points in treatment assignment. For instance, analyzing the impact of loyalty program benefits by comparing customers just above and below spending thresholds.
This method creates an artificial "synthetic" control unit by combining multiple untreated units to best match the pre-treatment characteristics of the treated unit. In marketing, this might involve creating a synthetic control market by weighting several non-campaign markets to mirror the treatment market's historical performance, then comparing post-campaign outcomes to estimate true incremental impact.
This approach compares changes in outcomes over time between treatment and control groups, helping isolate causal effects from broader market trends.
Causal inference principles enhance Marketing Mix Modeling by better isolating the incremental impact of different marketing channels, accounting for base sales, seasonality, and external factors.
Moving beyond last-click attribution, causal inference helps determine which touchpoints genuinely influence conversion decisions rather than simply being present in the customer journey.
Causal inference methods help measure the true incremental impact of marketing campaigns by comparing outcomes between exposed and unexposed audiences.
By establishing causal relationships between marketing spend and business outcomes, organizations can make more informed budget allocation decisions across channels and campaigns.
When treatment and control groups differ systematically, it becomes difficult to isolate causal effects. Online advertising faces particular challenges here, as ad targeting can create non-random exposure patterns.
Results from causal inference studies may not generalize across different time periods, markets, or customer segments, requiring careful consideration of context.
Establishing causation often requires specific data structures, controlled conditions, or long observation periods that may not always be available in marketing contexts.
Before analysis, clearly articulate the suspected causal relationship and potential confounding factors that need to be addressed.
Select causal inference techniques based on available data, business constraints, and the specific research question at hand.
When possible, use multiple causal inference methods to validate findings and increase confidence in results.
Marketing effects often occur with delays, so causal inference analysis should consider appropriate time windows for impact measurement.
Test causal inferences against out-of-sample data or holdout periods to ensure findings are robust and reliable.
As privacy regulations limit traditional tracking methods and marketing attribution becomes more complex, causal inference techniques are becoming increasingly important for marketing measurement. Machine learning approaches are making these methods more accessible and scalable for marketing teams.
Organizations that master causal inference will have significant advantages in understanding true marketing effectiveness, optimizing budget allocation, and demonstrating genuine business impact from marketing investments.
Dr. Paavo Niskala is a Principal Engineer at Sellforte. With PhD in the field of computational plasma physics, he has over 10 years of experience in designing and building complex data-intensive systems. Paavo has especially focused on using data science in critical business applications, such as Marketing Mix Modeling, which helps businesses make better marketing decisions. Follow Paavo in LinkedIn.