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Difference-in-differences | Synthetic Control | Causal Inference in Data Science Part 2

Emma Ding2022-01-31
data science interview#data science#data scientist#casual inference#Difference-in-differences#Synthetic Control
15K views|2 years ago
💫 Short Summary

In this video, the speaker discusses the challenge of measuring the causal impact of rare events, such as the effect of COVID-19 on the economy, and introduces the difference-in-differences and synthetic control methods as solutions. The speaker also provides a practical example of using the Uber Express Pool feature to demonstrate how these methods can be applied to measure the impact of a new feature in data science.

✨ Highlights
📊 Transcript
Yuan discusses the challenge of measuring the impact of rare events, such as the example of the effect of COVID-19 on the economy.
00:00
In order to use methods like regression or matching, we need data with a large number of treated and untreated units, which is not always practical for rare events that impact large units.
To measure the impact of COVID-19 on the economy, we can create a parallel universe in which COVID-19 never happened, and compare the outcomes to determine the causal impact.
The speaker provides an example of using data science to measure the impact of a new feature, Uber Express Pool, on trip durations for drivers and riders.
04:11
The success of the new feature can be measured by looking at the impact on trip durations for drivers and riders.
The average trip duration is chosen as a success metric for the new feature.
The problem of attributing changes in trip durations to the new feature or external factors is addressed by using the difference-in-differences method.
The speaker explains how to find the treatment effect of the Uber Express Pool feature using the difference-in-differences method by making an assumption that the trend in the outcome metric would be the same in two cities without the feature.
08:52
The speaker describes the trend in the average trip duration before and after the feature launch in San Francisco and New York City.
The difference in trip duration trends is considered the treatment effect of the feature.
Another way to analyze the treatment effect is by breaking down the common trend assumption and observing the differences in the outcome metrics.
The method of synthetic control is discussed as a way to measure the impact of an event by creating a counterfactual using the outcomes of similar untreated units.
15:12
Synthetic control involves finding untreated units that are similar to the treated unit and combining their outcomes to create a counterfactual.
The weights for the untreated units in the donor pool are found from the pre-treatment period data, and the synthetic control is the weighted average of the donor pool.
The method of synthetic control has limitations, and if the assumptions do not hold, more sophisticated methods may be needed.
The speaker explains how the weights for the untreated cities in the synthetic control method are found from the pre-treatment period data by minimizing the difference between the predicted and actual outcomes.
18:10
Models are built to predict the outcome using features such as rain and city population, and the more predictive features have higher importances.
The next step is to minimize the average of the predicted outcomes in the untreated cities and the actual outcomes in the treated city in the pre-treatment period.
The weights for the untreated cities are found by minimizing the difference between the predicted and actual outcomes.
💫 FAQs about This YouTube Video

1. How can we use causal inference to measure the impact of a rare event like COVID-19 on the economy?

To measure the impact of a rare event like COVID-19 on the economy, we can use causal inference by creating a parallel universe in which the event never happened. The difference between the outcomes in the actual world and this \"counterfactual\" world represents the causal impact of the event on the economy. This can be done using methods such as difference in differences and synthetic control to draw conclusions about the event's effects.

2. What are the two methods used to create a counterfactual world in the context of data science?

The two methods used to create a counterfactual world in data science are \"difference in differences\" and \"synthetic control.\" Difference in differences involves comparing the change in outcomes over time between a treated group and an untreated group. Synthetic control, on the other hand, constructs a counterfactual outcome for a treated unit by combining outcomes from similar untreated units.

3. How are the weights for untreated cities found in the synthetic control method?

The weights for untreated cities in the synthetic control method are found by minimizing the difference between the predicted and actual outcomes in the pre-treatment period. This is done by using features such as city population and weather to predict the outcome, and assigning higher weights to more important and predictive features.

4. What is the main idea behind the synthetic control method in causal inference?

The main idea behind the synthetic control method in causal inference is to create a counterfactual outcome for a treated unit by combining outcomes from similar untreated units. This method allows researchers to analyze the effect of an intervention or treatment by comparing the actual outcome for the treated unit with the synthetic counterfactual outcome.