4. Causal Inference¶
Causal inference is a vast field that seeks to address questions relating causes to effects. We more formally define causal inference as the study of how a treatment (i.e., action or intervention) affects outcomes of interest relative to an alternate treatment. Often, one of the treatments represents a baseline or status quo: it is then called a control. Academics have used causal reasoning for over a century to establish many scientific findings we now consider as facts. In the past decade, there has been a rapid increase in the adoption of causal thinking by firms, and it is now an integral part of data science. Causal inference can be used to answer such questions as:
What is the effect on the throughput time (outcome) of introducing a new drill to a production line (treatment) relative to the current process (control)?
What is the effect of changing the text on the landing page’s button (treatment) on the clickthrough rate (outcome), relative to the current text (control)?
Which of two hospital admission processes (treatment 1 vs treatment 2) leads to better health results (outcome)?
The most reliable way to establish causal relationships is to run a randomized experiment. Different fields have different names for these, including A/B tests, clinical trials, randomized control trials, etc… but basically, a randomized experiment involves randomly assigning subjects (e.g., customers, divisions, companies) to either receive a treatment or a control intervention. The effectiveness of the treatment is then assessed by contrasting the outcomes of the treated subjects to the outcomes of the control subjects.
The simple idea of running experiments has had a profound impact on how managers make decisions, as it allows them to discern their customers’ preferences, evaluate their initiatives, and ultimately test their hypotheses. Experimentation is now an integral part of the product development process at most technology companies. It is increasingly being adopted by non-technology companies as well, as they recognize that experimentation allows managers to continuously challenge their working hypotheses and perform pivots that ultimately lead to better innovations.
Unfortunately, we cannot always run experiments because of ethical concerns, high costs, or an inability to control the random assignment directly. Luckily, this is a challenge that academics have grappled with for a long time, and they have developed many different strategies for identifying causal effects from non-experimental (i.e., observational) data. For example, we know that smoking causes cancer even though no one ever ran a randomized experiment to measure the effect of smoking. However, it is important to understand that causal claims from observational data are inherently less reliable than claims derived from experimental evidence and can be subject to severe biases.