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How do you handle complex causal relationships?

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Complex causal relationships are often encountered in statistics, especially when you want to estimate the effect of an intervention or a treatment on an outcome of interest. However, simply observing the correlation between two variables does not imply causation, and there may be confounding factors, mediators, moderators, or feedback loops that affect the causal mechanism. How do you handle these challenges and draw valid causal inferences from your data? In this article, you will learn some basic concepts and methods for dealing with complex causal relationships.

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1 What is causal inference?

Causal inference is the process of inferring causal effects from observational or experimental data. A causal effect is the difference in the outcome that would have occurred if the treatment had been applied versus if it had not been applied, holding everything else constant. This difference is also called the counterfactual, because it involves imagining what would have happened under a different scenario. Causal inference aims to estimate the counterfactual from the available data, using assumptions, models, and techniques that account for the complexity of the causal system.

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Jyotishko Biswas

AI and Gen AI Leader | AI Speaker | 18 years exp. in AI | AI Leader Award 2024 (from 3AI) | Indian Achievers Award 2024 (from Indian Achievers Forum) | Forbes Technology Council Member | ex Deloitte, IBM
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Causal inference is one of the fundamental concepts of data science. It focuses on cause and effect relationship between variables. Its the impact of change in feature variable on response variable (I am keeping it simple), while we assume everything else in the environment remains constant. These quantitative impacts can be found from different models (for example regression models). The quantitative impacts also help business people to make decisions. For example if we know how much will be change in revenue for 1M USD increase in marketing activity, then the marketing leader can take a more informed decision.

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Tobe M.

Data Scientist | Founder | Data Advisor, Educator & Coach | Public Speaker | CompTIA Data SME
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Causal inference is one of the most effective advanced measurement technique used in measuring the true impact or uplift of marketing activities. It helps advertisers to measure causal effects of their ad spend. Causal inference provides a more rigorous and reliable way to measure the impact of marketing campaigns compared to simple before-and-after comparisons. It helps advertisers make informed decisions about their marketing strategies. Imagine you're running a marketing campaign and want to know how effective your new advertising campaign was in boosting sales. Causal inference can help you determine the true impact of your campaign without relying on guesswork or simply comparing sales before and after the campaign.

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2 What are the challenges of causal inference?

One of the main challenges of causal inference is the presence of confounding factors, which are variables that affect both the treatment and the outcome, and can bias the estimation of the causal effect. For example, if you want to measure the effect of smoking on lung cancer, you need to control for other factors that may influence both smoking and lung cancer, such as age, gender, or genetics. Another challenge is the existence of mediators, which are variables that transmit the effect of the treatment to the outcome, and moderators, which are variables that modify the effect of the treatment depending on their values. For instance, if you want to measure the effect of education on income, you need to consider how education affects skills, which in turn affect income, and how education may have different effects for different groups of people. A third challenge is the possibility of feedback loops, which are situations where the outcome affects the treatment or vice versa, creating a dynamic and non-linear causal system. For example, if you want to measure the effect of social media on mental health, you need to account for how mental health affects social media use and how social media use affects mental health over time.

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3 How do you handle complex causal relationships?

The answer to this question is complex, as different causal problems may require different approaches and assumptions. To handle complex causal relationships, it is important to define your research question and identify your treatment, outcome, and potential confounders. Additionally, you should draw a causal diagram or graph that represents the system, and use it to assess the identifiability and testability of the causal effect. Furthermore, the diagram can be used to explore the mechanisms and interactions of the effect. Statistical methods such as regression, matching, or propensity score can be used to adjust for confounding factors and estimate the average causal effect. Sensitivity analysis, instrumental variables, or randomization can also be used to check the robustness of your effect and quantify any potential bias. Finally, methods such as mediation analysis, moderation analysis, or structural equation modeling can be used to decompose the effect into direct and indirect effects.

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Tobe M.

Data Scientist | Founder | Data Advisor, Educator & Coach | Public Speaker | CompTIA Data SME
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Though Causal inference assumes linear relationship between test and control group, there are some cases where we encounter complex causal relationships that are non-linear. One of the ways to handle more complex causal relationships is by building a non-linear statistical models like random forest which have the ability to capture more complex relationships between variables. These models can better represent non-linear relationships and interactions between the test group, control group, and other factors influencing sales. Also, ML techniques, such as causal trees or causal forests algorithms, to model complex causal relationships and identify non-linear interactions between variables.

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4 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 do you handle complex causal relationships?

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1 What is causal inference?

2 What are the challenges of causal inference?

3 How do you handle complex causal relationships?

4 Here’s what else to consider

Statistics

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How do you handle complex causal relationships?

1

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1 What is causal inference?

2 What are the challenges of causal inference?

3 How do you handle complex causal relationships?

4 Here’s what else to consider

Statistics

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