What-if and How-to queries are fundamental data analysis questions that provide insights about the effects of a hypothetical update without actually making changes to the database. Traditional systems assume independence across differ¬ent tuples and non-updated attributes of the database. However, different attributes and tuples are generally dependent in real-world scenarios. We propose to demonstrate HypeR, a novel system to efficiently answer what-if and how-to queries while capturing causal dependencies among different attributes and tuples in the database. To compute the results, HypeR leverages a suite of optimizations along with techniques from causal inference to effectively estimate the answers. HypeR allows users to formulate complex hypothetical queries by using a novel SQL-like syntax and presents the output as interactive visualizations that can be explored and analyzed with ease.