Counterfactual Inference Using Time Series Data
<p>Are you ever curious about the true impact of a new marketing campaign, product launch, new government policy, or some other event? Wouldn’t it be nice if you could compare the results of the event occurring versus never occurring at all? Well, with counterfactual inference using time series data, you can do just that! And the best part? You don’t need to be an actual time-traveling wizard — just a few lines of Python code from the tfcausalimpact package will suffice.</p>
<p>In this article, we’re going to take a deep dive into counterfactual inference using time series data. We’ll start with a quick primer on causal inference, followed by some real-world applications of counterfactual inference with time series data. And finally, we’ll wrap things up with a demo in Python, using the tfcausalimpact package.</p>
<p>By the end, you’ll have a solid understanding of this powerful technique and the tools you need to start using it in your own data science projects. So grab a cup of coffee and let’s get started!</p>
<h2>2) Quick Causal Inference Primer</h2>
<p>Before we dive in to CausalImpact analysis, let’s make sure we’re all on the same page. Here are five must-know things about causal inference to kickstart our journey.</p>
<p><a href="https://medium.com/@ThatShelbs/counterfactual-inference-using-time-series-data-83c0ef8f40a0">Website</a></p>