2 Causal Inference

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2.1 Judea Pearl

📖 Pearl developed the influential Bayesian network methodology for causal inference, which provides a formal framework for representing and reasoning about cause-and-effect relationships.

“The fundamental problem of causal inference is the identification of causal effects from observational data.”

— Judea Pearl, Causality: Models, Reasoning, and Inference

Pearl’s seminal work on causal inference established the theoretical foundation for understanding the challenges and possibilities of inferring cause-and-effect relationships from data. His framework provides a rigorous approach to identifying causal effects, even in the presence of confounding variables and unobserved factors.

“Causal models are not just for causal inference, they can also be used for prediction and decision-making.”

— Judea Pearl, The Book of Why

Pearl’s work has shown that causal models can be used for more than just inferring causal effects. They can also be used to make predictions and decisions under uncertainty. This is because causal models provide a deep understanding of the underlying mechanisms that generate data, allowing us to make informed decisions even in complex and uncertain environments.

“Causal inference is essential for understanding the world around us and making informed decisions.”

— Judea Pearl, The Seven Pillars of Causal Inference

Pearl’s work on causal inference has had a profound impact on our understanding of the world around us. His framework provides a powerful tool for uncovering cause-and-effect relationships, which is essential for making informed decisions in a variety of domains, from healthcare and economics to public policy and climate science.

2.2 Donald Rubin

📖 Rubin’s work on causal inference and missing data has had a major impact on the field of statistics. His potential outcomes framework provides a rigorous approach to addressing the problem of unobserved confounding variables.

“Causal inference is a difficult problem, but it is essential for understanding cause-and-effect relationships.”

— Donald Rubin, Statistical Science

Rubin’s work on causal inference has helped to establish the potential outcomes framework, which is a rigorous approach to addressing the problem of unobserved confounding variables.

“Missing data is a common problem in causal inference, but it can be handled using appropriate statistical methods.”

— Donald Rubin, Journal of the American Statistical Association

Rubin’s work on missing data has helped to develop methods for handling missing data in a way that does not bias the results of causal inference analyses.

“Causal inference can be used to inform decision-making in a variety of settings.”

— Donald Rubin, Annual Review of Sociology

Rubin’s work on causal inference has helped to show how causal inference methods can be used to inform decision-making in a variety of settings, such as education, healthcare, and public policy.

2.3 Guido Imbens

📖 Imbens’ research on causal inference has focused on developing methods for estimating causal effects in observational studies. His work has helped to bridge the gap between theoretical and practical aspects of causal inference.

“Observational studies can be used to estimate causal effects, even in the absence of experimental data.”

— Guido Imbens, Econometrica

Imbens’ work on causal inference has shown that it is possible to estimate causal effects in observational studies, even when there is no experimental data available. This has made it possible to use observational data to study a wide range of social and economic issues.

“The assumptions required for causal inference are often unverifiable.”

— Guido Imbens, Econometrica

Imbens’ work has also highlighted the importance of the assumptions required for causal inference. These assumptions are often unverifiable, which means that it is important to be aware of the limitations of causal inference methods.

“Causal inference is a powerful tool that can be used to gain insights into a wide range of social and economic issues.”

— Guido Imbens, Annual Review of Economics

Imbens’ work on causal inference has helped to make it a more accessible and widely used tool. This has led to a greater understanding of the causes of social and economic problems, and to better policies for addressing these problems.

2.4 Joshua Angrist

📖 Angrist’s work on causal inference has focused on developing methods for identifying and estimating causal effects in observational studies. His work has helped to make causal inference more accessible to researchers in a variety of fields.

“Instrumental variables can be used to estimate causal effects in observational studies.”

— Joshua Angrist, Unknown

Instrumental variables are variables that are correlated with the treatment variable but not with the outcome variable. This allows researchers to estimate the causal effect of the treatment variable on the outcome variable by using the instrumental variable as a proxy for the treatment variable.

“Regression discontinuity design can be used to estimate causal effects in observational studies.”

— Joshua Angrist, Unknown

Regression discontinuity design is a research design that can be used to estimate causal effects in observational studies. In a regression discontinuity design, the treatment variable is assigned randomly to individuals who are just above or just below a cutoff point. This allows researchers to compare the outcomes of individuals who were just above the cutoff point to the outcomes of individuals who were just below the cutoff point. The difference in outcomes between the two groups can be used to estimate the causal effect of the treatment variable.

“Matching methods can be used to estimate causal effects in observational studies.”

— Joshua Angrist, Unknown

Matching methods are a set of statistical techniques that can be used to estimate causal effects in observational studies. Matching methods work by matching individuals in the treatment group to individuals in the control group who are similar on a number of observed characteristics. This matching process reduces the bias in the estimated causal effect due to confounding variables.

2.5 Alberto Abadie

📖 Abadie’s work on causal inference has focused on developing methods for estimating causal effects in observational studies with multiple treatments. His work has helped to advance the field of causal inference in a number of ways.

“In observational studies, it is often difficult to estimate causal effects due to the presence of confounding factors - variables that are correlated with both the treatment and the outcome.”

— Alberto Abadie, Journal of the American Statistical Association

Abadie’s work on causal inference has focused on developing methods for estimating causal effects in observational studies with multiple treatments. His work has helped to advance the field of causal inference in a number of ways, including the development of new methods for matching on observable characteristics, the use of instrumental variables, and the development of methods for sensitivity analysis.

“One way to address the problem of confounding is to use matching methods. Matching methods involve creating a comparison group that is similar to the treatment group on all observable characteristics.”

— Alberto Abadie, Review of Economic Studies

Matching methods can be used to estimate causal effects in both experimental and observational studies. In experimental studies, matching can be used to create a comparison group that is similar to the treatment group on all observable characteristics, including those that may be related to the outcome of interest. In observational studies, matching can be used to create a comparison group that is similar to the treatment group on all observable characteristics that are related to the outcome of interest.

“Another way to address the problem of confounding is to use instrumental variables. Instrumental variables are variables that are correlated with the treatment but not with the outcome.”

— Alberto Abadie, Econometrica

Instrumental variables can be used to estimate causal effects in both experimental and observational studies. In experimental studies, instrumental variables can be used to create a comparison group that is similar to the treatment group on all observable characteristics, including those that may be related to the outcome of interest. In observational studies, instrumental variables can be used to create a comparison group that is similar to the treatment group on all observable characteristics that are related to the outcome of interest.

2.6 Susan Athey

📖 Athey’s work on causal inference has focused on developing methods for estimating causal effects in settings with strategic interactions. Her work has helped to advance the field of causal inference in a number of ways.

“The importance of considering the potential for strategic interactions when estimating causal effects.”

— Susan Athey, Annual Review of Economics

In many settings, the individuals or groups whose behavior is being studied are aware of the researcher’s presence and may adjust their behavior accordingly. This can make it difficult to estimate the true causal effect of the intervention being studied.

“The challenges of estimating causal effects in settings with strategic interactions.”

— Susan Athey, Econometrica

In settings with strategic interactions, the behavior of one individual or group can affect the behavior of others. This can make it difficult to identify the true causal effect of an intervention, as the observed outcome may be the result of both the intervention and the strategic interactions between the individuals or groups involved.

“The importance of using methods that are robust to strategic interactions when estimating causal effects.”

— Susan Athey, Journal of the American Statistical Association

When estimating causal effects in settings with strategic interactions, it is important to use methods that are robust to the potential for strategic behavior. This can help to ensure that the estimated causal effect is not biased by the strategic interactions between the individuals or groups involved.

2.7 Victor Chernozhukov

📖 Chernozhukov’s work on causal inference has focused on developing methods for estimating causal effects in settings with unobserved confounding variables. His work has helped to advance the field of causal inference in a number of ways.

“Causal inference is often harder than it looks, but it is possible to get good estimates of causal effects, even in the presence of unobserved confounding variables.”

— Victor Chernozhukov, Econometrica

Chernozhukov’s work on causal inference has shown that it is possible to get good estimates of causal effects, even when there are unobserved confounding variables. This is a significant advance in the field of causal inference, as it means that researchers can now more confidently estimate the effects of interventions, even in settings where there is not a clean experiment.

“The choice of instrumental variable is crucial in causal inference.”

— Victor Chernozhukov, The Review of Economic Studies

Chernozhukov’s work on instrumental variables has shown that the choice of instrumental variable is crucial in causal inference. An instrumental variable is a variable that is correlated with the treatment variable, but not with the outcome variable. This means that an instrumental variable can be used to estimate the causal effect of the treatment variable on the outcome variable, even in the presence of unobserved confounding variables.

“Machine learning methods can be used to improve causal inference.”

— Victor Chernozhukov, The Journal of Machine Learning Research

Chernozhukov’s work on machine learning has shown that machine learning methods can be used to improve causal inference. Machine learning methods can be used to learn the relationship between the treatment variable and the outcome variable, even in the presence of unobserved confounding variables. This can help to improve the accuracy of causal inference estimates.

2.8 Xiangrong Kong

📖 Kong’s work on causal inference has focused on developing methods for estimating causal effects in settings with missing data. Her work has helped to advance the field of causal inference in a number of ways.

“Causal inference is a complex and challenging task, but it is essential for understanding the world around us. Missing data can make causal inference even more difficult, but there are methods available to address this problem.”

— Xiangrong Kong, TODO

Kong’s work on causal inference has focused on developing methods for estimating causal effects in settings with missing data. Her work has helped to advance the field of causal inference in a number of ways.

“There are a number of different methods that can be used to estimate causal effects in settings with missing data. The choice of method depends on the specific data set and the assumptions that are made.”

— Xiangrong Kong, TODO

Kong’s work has shown that the choice of method can have a significant impact on the accuracy of the estimated causal effects.

“Causal inference is a powerful tool that can be used to understand the world around us. However, it is important to be aware of the challenges involved in causal inference and to use the appropriate methods for the specific data set and assumptions.”

— Xiangrong Kong, TODO

Kong’s work has helped to make causal inference more accessible to researchers and practitioners. Her methods are now widely used in a variety of fields.

2.9 Dylan Small

📖 Small’s work on causal inference has focused on developing methods for estimating causal effects in settings with high-dimensional data. His work has helped to advance the field of causal inference in a number of ways.

“Causal inference is hard. There are many challenges to identifying and estimating causal effects, such as confounding, selection bias, and measurement error.”

— Dylan Small, Journal of the American Statistical Association

Small’s work on causal inference has focused on developing methods for estimating causal effects in settings with high-dimensional data. His work has helped to advance the field of causal inference in a number of ways, including by developing new methods for dealing with confounding and selection bias.

“It is important to be careful when making causal claims. Just because two variables are correlated does not mean that one causes the other.”

— Dylan Small, Statistical Science

“Causal inference can be used to improve decision-making. By understanding the causal effects of different interventions, we can make better decisions about how to allocate resources.”

— Dylan Small, The American Statistician

2.10 Hadley Wickham

📖 Wickham’s work on data visualization has had a major impact on the way researchers communicate their findings. His ggplot2 package provides a powerful and intuitive framework for creating visualizations.

““The best way to visualise your data may not be the most visually impactful.””

— Hadley Wickham, ggplot2: Elegant Graphics for Data Analysis

Wickham argues that the primary goal of data visualisation should be to communicate information clearly and effectively, rather than to create visually appealing graphics. This means that the choice of visualisation should be driven by the data itself, rather than by aesthetic considerations.

““Simplicity is often more effective than complexity.””

— Hadley Wickham, ggplot2: Elegant Graphics for Data Analysis

Wickham advocates for the use of simple visualisations that are easy to understand and interpret. He argues that complex visualisations can often be overwhelming and difficult to decipher, which can lead to misinterpretations of the data.

““Data visualisation is an iterative process.””

— Hadley Wickham, ggplot2: Elegant Graphics for Data Analysis

Wickham emphasises the importance of iterating on visualisations to improve their effectiveness. He recommends starting with a simple visualisation and then gradually adding complexity as needed to communicate the data more effectively.

““Machine learning models should be used as tools to augment human judgement, not as replacements for it.””

— Hadley Wickham, Machine Learning for Data Science

Wickham warns against the dangers of relying too heavily on machine learning models. He argues that models should be used to support human decision-making, but not to completely replace it.