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The Beginner’s Guide to Causal Inference for Making Effective Business DecisionsWhat is Causal Inference? Learn how you can know what works and use it effectively to optimize business processes. A user-friendly guide to Causal Inference!Almost every one of you must have studied in your school the favorite mantra of statistical correlation, which was “correlation doesn’t imply causation”. Just because there is a high correlation between two phenomena, it doesn’t mean that there is causal attribution too. For example, there is a high correlation between sunrise and the call of the rooster; however, it doesn’t help us to know if it’s the call of the rooster that causes the sun to rise, or is it the rising of the sun that causes call of the rooster. Various businesses and industries operate with limited resources, so it is absolutely important for the managers to what causes the desired change. For example, which of the marketing campaigns is driving forward the sales? Which drug is more effective in treating headaches? Which app version leads to higher user retention? All of these questions are answered by what the researchers/economists/data scientists call causal inference. In this article, I am going to tell you what causal inference is, why you should care, how you can use causal inference for your work, the methods, and lastly what the important considerations one needs to have in mind while applying causal inference. Lots of stuff to unpack here, so grab a cup of coffee (or tea) and start sipping the knowledge right away! WHAT IS CAUSAL INFERENCEIn very simple words, causal inference is the study of cause-and-effect relationships. Those who practice causal inference ask questions such as does X cause Y, what are the effects of changing X on Y? For example, what is the effect of changing the format of the website on user retention? Do we see increased customer satisfaction when we use in-person conversations vs automated chats on the website? To answer these kinds of questions, we need to see two different words in parallel — a world in which the “intervention” is given to a person and one in which the intervention is not given to the person. Let us take an example of a very simple A/B Testing. Ideally, we would want to measure the behavior of a person, X, while using our website version A, and the behavior of the same person, X, while using our website version B, both at the same time. Now because we do not live in a world where there can be clones of a person who can visit the same thing at the same time, we cannot ideally measure the impact of website version A vs website version B. This is a problem. What do we do now? Enter counterfactual! WHAT IS COUNTERFACTUAL?The word counterfactual can be divided into two words counter and factual. Therefore, the meaning of counterfactual is something that is opposite to the factual in a given situation. For example, if a group of students in a university is given additional job training, then this group is called the real-factual group. The counterfactual is the IDENTICAL group that is not given the training. The question of counterfactual concerns itself with what would have happened if the group had not received the training. However, we cannot give and not give training to the same student at the same time, we need an ESTIMATE of the counterfactual, that is to say, a group that is as close as possible to the group that is being trained. This group is also called the control group, that is, a group that is controlled and does not have the training. Sounds complex? Let me try to explain with the help of an example. Let’s say we work for a bank that has the details about the demographics and income data of individuals having accounts in the bank. We would like to roll out a new personal service to those customers who earn more than one million rupees a year to help them understand the various products by the bank and know if that personal service leads to the uptake of other financial products. Therefore, the intervention is personal service, the outcome is uptake as measured by the amount of financial product purchased. The estimated counterfactual group will be the group that is as similar as possible to the original group. How will you define this group? Let’s proceed further! WHY SHOULD I CARE ABOUT CAUSAL INFERENCE?The most important reason is that our world works in cause-and-effect relationships. Rising of the sun casues the rooster to make a call. Choosing a particular angle while hitting the ball causes the ball to fall in the hole. The second important reason is that the interactions of the our users with our products are not random, but deliberate and thought-out. Unless we know why they do what they do, we will not be able to offer them what they need. Thus, it requires us to have empathy for our users and then test our methodoligically what works and under what conditions. The third is that with the rise of machine learning, it is possible for us to have even more nuanced approach to causal infernece for making business decisions. Machines are great at doing repeatable fast tasks, but they’re still not great doing tasks that require an understanding of causes-and-effects. With the world having limited resources, we need to test out and then roll out policies that are most-impactful; the field of econometrics allow us to do that. HOW DO I USE CAUSAL INFERENCEThe field of Econometrics concerns itself with the structured study of Causal Inference. However, the whole of the knowledge can be boiled down to its core following steps.
METHODS OF CAUSAL INFERENCEOne of the most influential figures in the field of Causal Inference, Joshua Angrist, has coined the term “Furious Five” to describe the five most frequently used methods of causal inference. For the sake of simplicity, I will mention only those five here (alongwith short descriptions); however, there are many more methods with their intricacies that a researcher/data scientist can use for their work. I will suggest the motivated learner open any influential econometrics journal and find out more methods. Alright, what are the furious five?
As mentioned earlier, these are not the only methods of causal inference. There are some highly advanced methods such as Synthetic Control Methods, Bayesian Optimization, and Interrupted Time Series Design. THINGS TO KEEP IN MINDWith all the promises, you must be feeling excited to try out causal inference on your data right away. However, there are important considerations you need to keep in mind while using the methods. The first and foremost is that correlation between two variables doesn’t mean causation. Apart from this, there are four more that require special mention.
NEXT STEPSIf you have reached this line reading this article, it means you are very interested in learning more about causal inference and the methods that are used for causal analysis. Three of my favorite books are:
If you are interested in knowing more, or if you are a practitioner who would like to network, this is my LinkedIn profile. I will be happy to connect. Which of the following is the best description of a causal inference quizlet?Which of the following is the best description of a causal inference? there is a perfect statistical relationship between the variables.
Which of the following is the best description of the contribution of economics to the field organizational behavior?Which of the following is the best description of the contribution of economics to the field organizational behavior (OB)? It helps explain motivation, learning, and decision making.
Which of the following is required in order to establish a causal inference between two variables quizlet?Making causal inferences requires establishing three things. First, that the two variables are correlated; second, that the presumed cause precedes the presumed effect in time; and third, that no alternative explanation exists for the correlation.
Which of the following is the best description of the contribution of anthropology research to the field organizational Behaviour OB )?C. Which of the following is the best description of the contribution of anthropology research to the field organizational behavior (OB)? A. It helps explain differences among people from different cultures and their impact on organizational culture.
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