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<p>Thousands of years ago, a revolution happened. Humans started to realise that certain things cause others and that changes in the former lead to changes in the later. From this discovery, came science-based civilisation. <strong>All of this occurred, because humans asked a single question: Why?</strong> (Judea and Mackenzie, 2018).</p>

<p>Causal inference takes this question seriously, and it provides a mathematical language to articulate causal relationships that are already known, as well as those we wish to find out. As a result, this new science unleashed a wealth of robust methods for combining our knowledge with data, enabling us to answer causal questions like these: </p>

<ul>
 <li>Was it the aspirin that stopped my headache?</li>
 <li> How effective is a specific treatment in preventing a particular disease?</li>
 <li>Did the new tax law cause sales to go up, or was the advertising campaign?</li>
 <li>What is the health care cost attributable to obesity?</li>
 <li>Does smoking cause lung cancer?</li>
</ul>

These questions go beyond statistical dependencies, and traditional statistical methods cannot answer them.

<h2>Why?</h2>

<p>Although big data has been pointed out as the solution to various problems, data alone can only tell, for instance, that people who took a medicine recovered faster than those who didn't, but they cannot tell us why. As computing systems are frequently intervening to improve people' lives, it is critical to understand the effects of these interventions. Furthermore, traditional machine learning methods are often insufficient for causal analysis.</p>


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