Causal questions are ubiquitous in data science. For e.g. questions such as, did changing a feature in a website lead to more traffic or if digital ad exposure led to incremental purchase are deeply rooted in causality.

Randomized tests are considered to be the gold standard when it comes to getting to causal effects. However, experiments in many cases are unfeasible or unethical. In such cases one has to rely on observational (non-experimental) data to derive causal insights. The crucial difference between randomized experiments and observational data is that in the former, test subjects (e.g. customers) are randomly assigned a treatment (e.g. digital advertisement exposure). This helps curb the possibility that user response (e.g. clicking on a link in the ad and purchasing the product) across the two groups of treated and non-treated subjects is different owing to pre-existing differences in user characteristic (e.g. demographics, geo-location etc.). In essence, we can then attribute divergences observed post-treatment in key outcomes (e.g. purchase rate), as the causal impact of the treatment.

This treatment assignment mechanism that makes causal attribution possible via randomization is absent though when using observational data. Thankfully, there are scientific (statistical and beyond) techniques available to ensure that we are able to circumvent this shortcoming and get to causal reads.

The aim of this talk, will be to offer a practical overview of the above aspects of causal inference -which in turn as a discipline lies at the fascinating confluence of statistics, philosophy, computer science, psychology, economics, and medicine, among others. Topics include:

• The fundamental tenets of causality and measuring causal effects.
• Challenges involved in measuring causal effects in real world situations.
• Distinguishing between randomized and observational approaches to measuring the same.
• Provide an introduction to measuring causal effects using observational data using matching and its extension of propensity score based matching with a focus on the a) the intuition and statistics behind it b) Tips from the trenches, basis the speakers experience in these techniques and c) Practical limitations of such approaches
• Walk through an example of how matching was applied to get to causal insights regarding effectiveness of a digital product for a major retailer.
• Finally conclude with why understanding having a nuanced understanding of causality is all the more important in the big data era we are into.

{ This is a handson workshop . The use case is Traffic analysis . }

Most machine learning models assume independent and identically distributed (i.i.d) data. Graphical models can capture almost arbitrarily rich dependency structures between variables. They encode conditional independence structure with graphs. Bayesian network, a type of graphical model describes a probability distribution among all variables by putting edges between the variable nodes, wherein edges represent the conditional probability factor in the factorized probability distribution. Thus Bayesian Networks provide a compact representation for dealing with uncertainty using an underlying graphical structure and the probability theory. These models have a variety of applications such as medical diagnosis, biomonitoring, image processing, turbo codes, information retrieval, document classification, gene regulatory networks, etc. amongst many others. These models are interpretable as they are able to capture the causal relationships between different features .They can work efficiently with small data and also deal with missing data which gives it more power than conventional machine learning and deep learning models.

In this session, we will discuss concepts of conditional independence, d- separation , Hammersley Clifford theorem , Bayes theorem, Expectation Maximization and Variable Elimination. There will be a code walk through of simple case study.

Data Science is all about deriving actionable insights through data analysis.
There is no denying the fact that such insights have a tremendous business value.
But what if -
Some crucial data has been left out of consideration ?
Wrong inferences have been drawn during analysis ?
Results have been graphically misrepresented?
Imagine the adverse impact on your business if you take wrong decisions based on such cases.

In this talk we will discuss the following 5 key pitfalls to lookout for in the data analysis results before you take any decisions based on them
1. Selection Bias
2. Survivor Bias
3. Confounding Effects
4. Spurious Correlations
5. Misleading Visualizations

These are some of the most common points that are overlooked by the beginners in Data Science.

The talk will draw upon many examples from real life situations to illustrate these points.