LinkedIn, the world’s biggest professional network on the Internet, considers user experience its number one priority. The team at LinkedIn pays a lot of attention to determining the need for its product, its features, and the key impact on customer satisfaction due to any changes made to it. It is very necessary to know the causal influence of a product on its key standards to analyze the future of a product or an attribute. However, observational causal inference is not much popular and easily accessible.
Observational causal inference
Observational causal inference refers to figuring out the causal relationship between variables in a study. It is completely based on observations rather than measured experiments. In simple terms, it is a way to infer the effect of one variable on other based-on observations of the two variables in a population rather than through experimental operation. Applying observational causal inference to understand the aspects influencing various outcomes is very useful.
Though A/B testing is the major technique for finding causality, in some cases, it is too expensive and unachievable. The team has pointed out a few instances where A/B testing doesn’t work well. It says that randomizing certain things isn’t a good idea for determining the causal effects. Those are –
- The impact of bugs on user experience can’t be defined by A/B testing as it cannot be random.
- The influence of externally originating shocks on a country’s economy can’t be treated randomly.
- The impact of radio and television campaigns cannot be jumbled at the user level.
For people with no coding backgrounds and for a convenient process of successively executing an observational causal study, LinkedIn has come up with Ocelot. Ocelot is an internal web application that delivers fast and robust solutions. Ocelot consists of two primary platforms –
- Ocelot web app – a combination of the user interface and the web services offered by Ocelot.
- Ocelot pipelines
Ocelot web app
The Ocelot web app offers quality and definitive causal studies and a UI layer validation to prevent incorrect configuration. It allows the user to access a thorough report of the causal study for a huge business impact. It has the capability to deliver a directed form to the users to make them understand the type of output metrics that are calculated, the treatment labels, and the period for each evaluated variable. The greatest feature of the web app is its ability to check robustness. The Ocelot platform has automated the process of checking robustness which increases the sureness in the calculated treatment influence if it is passed.
The second significant component is the Ocelot pipelines. These are fully consolidated and include Java jobs, Spark jobs, and R jobs. The main purpose is to understand the user configuration and gather all the data required for modeling and execution.
In consequence, LinkedIn’s new platform is a great approach to acquiring a detailed observational causal inference in less time and at scale. It is an effective alternative that overcomes the demerits of A/B testing and estimates the effects of product changes on business growth and the future evolution of the product for a profitable outcome.
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.