Causal Inference: Everything You Need to Know When Assessing Causal Inference Skills

Causal Inference

Causal inference is the process of drawing conclusions about the causal relationship between two or more variables. It is a complex and challenging task, but it is essential for making informed decisions in many areas of life, including business, science, and policy.

Causal inference is different from correlation. Correlation simply means that two variables are related to each other, but it does not necessarily mean that one variable causes the other. For example, there is a strong correlation between ice cream sales and drownings, but this does not mean that ice cream causes drowning. It is possible that both ice cream sales and drownings are caused by a third variable, such as hot weather.

To infer causality, we need to control for all other possible explanations for the relationship between the two variables. This can be done through randomized controlled trials, natural experiments, or observational studies.

Randomized controlled trials are the gold standard for causal inference. In a randomized controlled trial, participants are randomly assigned to either a treatment group or a control group. The treatment group receives the intervention that we are interested in studying, while the control group does not. We then compare the outcomes of the two groups to see if the intervention has a causal effect.

Natural experiments are observational studies that occur naturally. For example, we might study the effect of a new law on crime rates. We would compare crime rates in states that have passed the law to crime rates in states that have not passed the law. If we see that crime rates have decreased in states that have passed the law, we can infer that the law has a causal effect on crime rates.

Observational studies are observational studies that are not randomized. For example, we might study the effect of smoking on lung cancer. We would compare the rates of lung cancer among smokers to the rates of lung cancer among nonsmokers. However, it is difficult to control for all other possible explanations for the relationship between smoking and lung cancer in an observational study. For example, smokers are more likely to be exposed to other harmful substances, such as secondhand smoke. Therefore, it is difficult to say definitively that smoking causes lung cancer.

Causal inference is a complex and challenging task, but it is essential for making informed decisions in many areas of life. By understanding the principles of causal inference, we can be more confident in the conclusions that we draw from our data.

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How can you accurately assess somebody's technical skills, like the same way across the board, right? We had devised a Tableau-based assessment. So it wasn't like a past/fail. It was kind of like, hey, what do they send us? Did they understand the data or the values that they're showing accurate? Where we'd say, hey, here's the credentials to access the data set. And it just wasn't really a scalable way to assess technical - just administering it, all of it was manual, but the whole process sucked!

Cole Brickley, Avicado (Director Data Science & Business Intelligence)

I wouldn't dream of hiring somebody in a technical role without doing that technical assessment because the number of times where I've had candidates either on paper on the CV, say, I'm a SQL expert or in an interview, saying, I'm brilliant at Excel, I'm brilliant at this. And you actually put them in front of a computer, say, do this task. And some people really struggle. So you have to have that technical assessment.

Mike Yates, The British Psychological Society (Head of Data & Analytics)

I was at WooliesX (Woolworths) and we used Alooba and it was a highly positive experience. We had a large number of candidates. At WooliesX, previously we were quite dependent on the designed test from the team leads. That was quite a manual process. We realised it would take too much time from us. The time saving is great. Even spending 15 minutes per candidate with a manual test would be huge - hours per week, but with Alooba we just see the numbers immediately.

Shen Liu, Logickube (Principal at Logickube)

We get a high flow of applicants, which leads to potentially longer lead times, causing delays in the pipelines which can lead to missing out on good candidates. Alooba supports both speed and quality. The speed to return to candidates gives us a competitive advantage. Alooba provides a higher level of confidence in the people coming through the pipeline with less time spent interviewing unqualified candidates.

Scott Crowe, Canva (Lead Recruiter - Data)

How can you accurately assess somebody's technical skills, like the same way across the board, right? We had devised a Tableau-based assessment. So it wasn't like a past/fail. It was kind of like, hey, what do they send us? Did they understand the data or the values that they're showing accurate? Where we'd say, hey, here's the credentials to access the data set. And it just wasn't really a scalable way to assess technical - just administering it, all of it was manual, but the whole process sucked!

Cole Brickley, Avicado (Director Data Science & Business Intelligence)

I wouldn't dream of hiring somebody in a technical role without doing that technical assessment because the number of times where I've had candidates either on paper on the CV, say, I'm a SQL expert or in an interview, saying, I'm brilliant at Excel, I'm brilliant at this. And you actually put them in front of a computer, say, do this task. And some people really struggle. So you have to have that technical assessment.

Mike Yates, The British Psychological Society (Head of Data & Analytics)

I was at WooliesX (Woolworths) and we used Alooba and it was a highly positive experience. We had a large number of candidates. At WooliesX, previously we were quite dependent on the designed test from the team leads. That was quite a manual process. We realised it would take too much time from us. The time saving is great. Even spending 15 minutes per candidate with a manual test would be huge - hours per week, but with Alooba we just see the numbers immediately.

Shen Liu, Logickube (Principal at Logickube)