What is Causal Inference in Machine Learning?

Causality is a common term that we often use in real-life. This is the relationship between any cause and its effects. When we try to make sense of the world, we frequently think in terms of cause and effect: if we can figure out why something happened, we can adjust our behavior to enhance future results. 

 

Causal inference is a statistical technique that allows our AI and machine learning systems to think in the same way. We're looking at data from a network of servers and want to know how changes in our network settings affect latency, so we utilize causal inference to make informed decisions about our network settings. 

 

But, machine learning algorithms, unlike humans, struggle to determine that causal inference, or the process of detecting the independent, genuine consequence of an event occurring inside a bigger system.

 

This article will be all about Causal inference and the problems of machine learning in causal inference along with which, we will study some use cases to understand how causal inference impacts machine learning. 

 

 

Understanding Causal effects from data

 

Identifying causal effects is an important component of scientific research, and it may be used to a broad range of topics, including understanding behavior in online systems, the consequences of social policies, and illness risk factors. 

 

As computers have become more pervasive in our daily lives, problems of cause-and-effect are becoming increasingly important in the design and data-driven evaluation of all the computer systems and applications we create.

 

For strong forecasts and trustworthy decision-making, machine learning based on correlational pattern recognition is insufficient. A possible road forward is provided by new methods to machine learning based on causal reasoning concepts. 

 

Causal machine learning approaches are based on the stable and independent mechanisms that regulate the behavior of a system being represented and are guided by combined formal reasoning over observations and auxiliary information about data collecting methodologies or other domain knowledge. 

 

As a result, these methodologies offer exogenous change resilience and accurate modeling of counterfactual or "what-if" situations, which are critical to scientific exploration, knowledge, and decision-making.

 

Issues of Machine Learning in Causal Inference:

 

Here are some of the problems of Machine learning, listen by IEEE:

 

1. Because it depends significantly on vast predetermined sets of data, machine learning has trouble understanding causation. Additionally, inputting sets with several examples improves precision. 

2. Machine learning frequently ignores information that humans rely on heavily: world interventions, domain shifts, temporal structure—we generally perceive these elements as a nuisance and try to design them out. Accordingly, the majority of machine learning's recent triumphs may be boiled down to large-scale pattern recognition on appropriately gathered independent and identically distributed (i.i.d.) data.

3. This independent and identically distributed data asserts that random observations in a set of data are not dependent on one another and have a constant chance of happening. It is commonly used in machine learning. When tossing dice, for example, each roll is independent of the preceding one. As a result, the chance of each result remains unchanged.

4. Machine learning engineers have historically trained their models on larger and larger collections of instances in order to apply i.i.d. The model is expected to correctly encode the general distribution of the issue into its parameters, based on the bigger sample sizes.

5. Causality can assist address machine learning's generalization challenge since it remains constant even when the distributions of a problem shift somewhat.

 

(Related reading: Applications of Machine Learning)

 

 

How does Causal inference work?

 

Almost everything we do is governed by causality principles. Most of us contemplate the consequences of our actions before making a choice, and criminal conviction is founded on the notion of being the cause of a crime (guilt) as decided by a jury. 

 

As a result, it's realistic to expect that in the future, incorporating causality in a world model will be a fundamental component of intelligent systems. The formalisms, methods, and procedures of causal inference, on the other hand, remain a niche subject that few investigate.

 

Here we learn more about this inferring.

 

Inferring caused by Randomized Controlled Trials (RCTs):

 

According to Ericsson, Randomized controlled trials (RCTs) or A/B testing are the best standards for determining causal effects. In RCTs, we may divide a population of people into two groups: treatment and control, giving therapy to one group and nothing to the other, and comparing the results of both. 

 

We can determine if the therapy was effective based on the difference in results between the two groups if the treatment and control groups aren't too distinct. However, we may not always be able to conduct such tests. 

 

While flooding half of our servers with a large number of requests would be a good approach to see how response time is affected, we can't assault mission-critical services with DDOS attacks. Instead, we use observational data to compare the differences between servers that receive a lot of requests and those that receive very few.

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Judea Pearl's technique for using statistics to make causal inferences (source)

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To use this graph, we must assume the Causal Markov Condition, which states that a node is independent of any variables that are not direct causes or direct effects of that node, subject to the set of all its direct causes. 

 

Simply expressed, it is the belief that this graph accurately depicts all of the true relationships between the variables. Donald Rubin's prospective outcomes framework is another prominent way of inferring causes from observational data. 

 

This technique does not explicitly rely on a causal network, but it does make a number of assumptions about the data, such as that there are no other causes save the ones we're looking at.

 

(Related read: 15 Statistical Terms for Machine Learning)

 

 

Use cases of Causal Inference:

 

Existing AI systems would be smarter and more efficient if they could understand cause and effect. "Imagine a robot that learns that dropping objects causes them to break. It wouldn't need to hurl thousands of vases down the floor to see what happens to them," says the author. 

 

Furthermore, understanding causation would aid us in developing business models as well as new startups dedicated to assisting businesses in better understanding their data. 

 

For example, we recently began a project to detect leads from various sources. We hope that causality will aid us in identifying new leads based on previously unconsidered factors.

 

Towards data science has listed out a few use cases that we are going to discuss below:

 

Use case 1:

 

In an e-commerce setting, we might figure out which individual aspect has the most influence on a customer's choice to buy a product. We may better deploy resources to enhance a certain KPI with this knowledge. 

 

We might also rate the importance of several aspects in making a purchase choice. We could tell if a consumer would have bought a certain product if he or she hadn't bought anything else in the previous two years.

 

Use case 2:

 

We frequently try to estimate if a farmer's crop production will be lower this year in the agricultural sector. Using causal inference, however, it will become clearer what efforts need to be taken to improve the yield. Beyond these prospective applications, the creation of additional causality in Machine Learning is a key step toward creating machine intelligence that is more human-like.

 

Use case 3: 

 

In a larger sense, we could figure out how and what bad consequences a certain company approach may have avoided. By developing a specialized training program for our business developers, we could also determine by how much we could expect our sales to improve the effectiveness of a particular training program.

 

(Recommended reading: Machine learning techniques)

 

 

Bottom Line:

 

As we've seen in the many instances given in this blog, a lack of understanding of the problem's causal structure can lead to incorrect conclusions. 

 

Because issues with causal inference are common in several socially significant domains of study, such as health, sociology, and finance, researchers must exercise extreme caution when validating any causal claims they make after examining the data. 

 

On a more practical level, it appears to us that understanding the level of the causal hierarchy our research issue belongs to is the easy but critical first step in performing any study. Indeed, without this knowledge, a researcher risks reaching incorrect findings, which might have disastrous implications, thus this phase should never be skipped.

 

(Also read: What is Predictive Modelling?)

 

This article is written to give the reader an idea about the basic concept of Causal inference and how it is related to Machine learning. Some use cases are also discussed to get a better understanding of the concept.