Tuesday, April 21, 2020

Experimentation and Causal inference: A Behavioral Economic Perspective

In my previous post I discussed the value proposition of experimentation and causal inference from a mainline economic perspective. In this post I want to view this from a behavioral economic perspective. From this point of view experimentation and causal inference can prove to be invaluable with respect to challenges related to overconfidence and decision making under uncertainty.

Heuristic Data Driven Decision Making and Data Story Telling

In a fast paced environment, decisions are often made quickly and often based on gut decisions. Progressive companies have tried as much as possible to leverage big data and analytics to be data driven organizations. Ideally, leveraging data would help to override biases and often gut instincts and ulterior motives that may stand behind a scientific hypothesis or business question. One of the many things we have learned from behavioral economics is that humans tend to over interpret data into unreliable patterns that lead to incorrect conclusions. Francis Bacon recognized this over 400 years ago:

"the human understanding is of its own nature prone to suppose the existence of more order and regularity in the world than it finds" 

Anyone can tell a story with data. And with lots of data a good data story teller can tell a story to support any decision they want, good or bad. Decision makers can be easily duped by big data, ML, AI, and various BI tools into thinking that their data is speaking to them. As Jim Manzi and Stefan Thomke state in Harvard Business Review in the absence of experimentation and causal inference

"executives end up misinterpreting statistical noise as causation—and making bad decisions"

Data seldom speaks, and when it does it is often lying. This is the impetus behind the introduction of what became the scientific method. The true art and science of data science is teasing out the truth, or what version of truth can be found in the story being told. I think this is where experimentation and causal inference are most powerful and create the greatest value in the data science space. John List and Uri Gneezy discuss this in their book 'The Why Axis.' 

"Big data is important, but it also suffers from big problems. The underlying approach relies heavily on correlations, not causality. As David Brooks has noted, 'A zillion things can correlate with each other depending on how you structure of the data and what you compare....because our work focuses on field experiments to infer causal relationships, and because we think hard about these causal relationships of interest before generating the data we go well beyond what big data could ever deliver."

Decision Making Under Uncertainty, Risk Aversion, and The Dunning-Kruger Effect

Kahneman (in Thinking Fast and Slow) makes an interesting observation in relation to managerial decision making. Very often managers reward peddlers of even dangerously misleading information (data charlatans) while disregarding or even punishing merchants of truth. Confidence in a decision is often based more on the coherence of a story than the quality of information that supports it. Those that take risks based on bad information, when it works out, are often rewarded. To quote Kahneman:

"a few lucky gambles can crown a reckless leader with a Halo of prescience and boldness"

The essence of good decision science it understanding and seriously recognizing risk and uncertainty. As Kahneman discusses in Thinking Fast and Slow, those that often take the biggest risks are not necessarily any less risk averse, they simply are often less aware of the risks they are actually taking.  This leads to overconfidence and lack of appreciation for uncertainty, and a culture where a solution based on pretended knowledge is often preferred and even rewarded. Its easy to see how the Dunning-Kruger effect would dominate. This feeds a viscous cycle that leads to collective blindness toward risk and uncertainty. It leads to taking risks that should be avoided in many cases, and prevents others from considering smarter calculated risks.  Thinking through an experimental design (engaging Kahneman's system 2) provides a structured way of thinking about business problems and all the ways our biases and the data can fool us..  In this way experimentation and causal inference can ensure a better informed risk appetite to support decision making.

Just as rapid cycles of experiments in a business setting can aid in the struggle with the knowledge problem, experimentation and causal inference can aid us in our struggles with biased decision making and biased data.  Data alone doesn't make good decisions because good decisions require something outside the data. Good decision science leverages experimentation and causal inference that brings theory and subject matter expertise together with data so we can make better informed business decisions in the face of our own biases and the biases in data.

A business culture that supports risk taking coupled with experimentation and causal inference will come to value a preferred solution over pretended knowledge. That's valuable. 

See also:

Monday, April 20, 2020

Experimentation and Causal Inference Meet the Knowledge Problem

Why should firms leverage experimentation and causal inference? With recent advancements in computing power and machine learning, why can't they simply base all of their decisions on predictions or historical patterns discovered in the data using AI?  Perhaps statisticians and econometricians and others have a simple answer. The kinds of learnings that will help us understand the connections between decisions and the value we create require understanding causality. This requires something that may not be in the data to begin with. Experimentation and causal inference may be the best (if not the only) way of answering these questions. In this series of posts I want to focus on a number of fundamental reasons that experimentation and causal inference are necessary in business settings from the perspective of both mainline and behavioral economics:

Part 1: The Knowledge Problem
Part 2:  Behavioral Biases
Part 3:  Strategy and Tactics

In this post I want to discuss the value of experimentation and causal inference from a basic economic perspective. The fundamental problem of economics, society, and business is the knowledge problem. In his famous 1945 American Economic Review article The Use of Knowledge in Society, Hayek argues:

"the economic problem of society is not merely a problem of how to allocate 'given resources'....it is a problem of the utilization of knowledge which is not given to anyone in its totality."

A really good parable explaining the knowledge problem is the essay I, Pencil by Leonard E. Read. The fact that no one person possesses the necessary information to make something that seems so simple as a basic number 2 pencil captures the essence of the knowledge problem.

If you remember your principles of economics, you know that the knowledge problem is solved by prices which reflect tradeoffs based on the disaggregated incomplete and imperfect knowledge and preferences of millions (billions) of individuals. Prices serve both the function of providing information and the incentives to act on that information. It is through this information creation and coordinating process that prices help solve the knowledge problem. Prices solve the problem of calculation that Hayek alluded to in his essay, and they are what coordinate all of the activities discussed in I, Pencil. 

In Living Economics: Yesterday, Today, and Tommorow by Peter J. Boettke, discusses the knowledge problem in the context of firms and the work of economist Murray Rothbard:

"firms cannot vertically integrate without facing a calculation problem....vertical integration eliminates the external market for producer goods."

 Coase, also recognized that as firms integrate to eliminate transactions costs they also eliminate the markets which generate the prices that solve the knowledge problem! This tradeoff has to be managed well or firms go out of business. In a way firms could be viewed as little islands with socially planned economies in a sea of market competition. As Luke Froeb masterfully illustrates in his text Managerial Economics: A Problem Solving Approach (3rd Ed), decisions within firms in effect create regulations, taxes, and subsidies that destroy wealth creating transactions. Managers should make decisions that consummate the most wealth creating transactions (or do their best not to destroy, discourage, or prohibit wealth creating transactions).

So how do we solve the knowledge problems in firms without the information creating and coordinating role of prices? Whenever mistakes are made, Luke Froeb provides this problem solving algorithm that asks:

1) Who is making the bad decision?
2) Do they have enough information to make a good decision?
3) Do they have the incentive to make a good decision?

In essence, in absence of prices, we must try to answer the same questions that market processes often resolve. And we could leverage experimentation and causal inference to address each of the questions above:

How do we know a decision was good or bad to begin with? 
How do we get the information to make a good decision? 
What incentives or nudges work best to motivate good decision making? 

What does failure to solve the knowledge problem in firms look like in practical terms? Failure to consummate wealth creating transactions implies money left on the table - but experimentation and causal inference can help us figure out how to reclaim some of these losses. List and Gneezy address this in The Why Axis:

"We think that businesses that don't experiment and fail to show, through hard data, that their ideas can actually work before the company takes action - are wasting their money....every day they set suboptimal prices, place adds that do not work, or use ineffective incentive schemes for their work force, they effectively leave millions of dollars on the table."

Going back to I, Pencil and Hayek's essay, the knowledge problem is solved through the spontaneous coordination of multitudes of individual plans via markets. Through a trial and error process where feedback is given through prices, the plans that do the best job coordinating peoples choices are adopted. Within firms there are often only a few plans compared to the market and these are in the form of various strategies and tactics. But as discussed in Jim Manzi's book Uncontrolled, firms can mimic this trial and error feedback process through iterative experimentation.

While experimentation and causal inference cannot perfectly emulate the same kind of evolutionary feedback mechanisms prices deliver in market competition, an iterative test and learn culture within a business may provide the best strategy for dealing with the knowledge problem. And that is one of many ways that experimentation and causal inference can create value.

Monday, April 6, 2020

Statistics is a Way of Thinking, Not Just a Box of Tools

If you have taken very many statistics courses you may have gotten the impression that it's mostly a mixed bag of computations and rules for conducting hypothesis tests or making predictions or creating forecasts. While this isn't necessarily wrong, it could leave you with the opinion that statistics is mostly just a box of tools for solving problems. Absolutely statistics provides us with important tools for understanding the world, but to think of statistics as 'just tools' can have some pitfalls (besides the most common pitfall of having a hammer and viewing every problem as a nail)

For one, there is a huge gap between the theoretical 'tools' and real world application. This gap is filled with critical thinking, judgment calls, and various social norms, practices, and expectations that differ from field to field, business to business, and stakeholder to stakeholder. The art and science of statistics is often about filling this gap. That's a stretch more than 'just tools.'

The proliferation of open source programming languages (like R and Python) and point and click automated machine learning solutions (like DataRobot and H2Oai) might give the impression that after you have done your homework in framing the business problem, data and feature engineering, then all that is left is hyper-parameter tuning and plugging and playing with a number of algorithms until the 'best' one is found. It might reduce to a mechanical (sometimes time consuming if not using automated tools) exercise. The fact that a lot of this work can in fact be automated probably contributes to the 'toolbox' mentality when thinking about the much broader field of statistics as a whole. In The Book of Why, Judea Pearl provides an example explaining why statistical inference (particularly causal inference) problems can't be reduced to easily automated mechanical exercises:

"path analysis doesn't lend itself to canned programs......path analysis requires scientific thinking as does every exercise in causal inference. Statistics, as frequently practiced, discourages it and encourages "canned" procedures instead. Scientists will always prefer routine calculations on data to methods that challenge their scientific knowledge."

Indeed, a routine practice that takes a plug and play approach with 'tools' can be problematic in many cases of statistical inference. A good example is simply plugging GLM models into a difference-in-differences context. Or combining matching with difference-in-differences. While we can get these approaches to 'play well together' under the correct circumstances its not as simple as calling the packages and running the code. Viewing methods of statistical inference and experimental design as just a box of tools to be applied to data could leave one open to the plug and play fallacy. There are times you might get by with using a flathead screwdriver to tighten up a phillips head screw, but we need to understand that inferential methods are not so easily substituted even if it looks like a snug enough fit on the surface.

Understanding the business problem and data story telling are in fact two other areas of data science that would be difficult to automate . But don't let that fool you into thinking that the remainder of data science including statistical inference is simply a mechanical exercise that allows one to apply the 'best' algorithm to 'big data'. You might get by with that for a minority set of use cases that require a purely predictive or pattern finding solution but the remainder of the world's problems are not so tractable. Statistics is about more than data or the patterns we find in it. It's a way of thinking about the data.

"Causal Analysis is emphatically not just about data; in causal analysis we must incorporate some understanding of the process that produces the data and then we get something that was not in the data to begin with." - Judea Pearl, The Book of Why

Statistics is A Way of Thinking

In their well known advanced text book "Principles and Procedures of Statistics, A Biometrical Approach", Steel and Torrie push back on the attitude that statistics is just about computational tools:

"computations are required in statistics, but that is arithmetic, not mathematics nor statistics...statistics implies for many students a new way of thinking; thinking in terms of uncertainties of probabilities.....this fact is sometimes overlooked and users are tempted to forget that they have to think, that statistics cannot think for them. Statistics can however help research workers design experiments and objectively evaluate the resulting numerical data."

At the end of the day we are talking about leveraging data driven decision making to override biases and often gut instincts and ulterior motives that may stand behind a scientific hypothesis or business question.  Objectively evaluating numerical data as Steel and Torrie put it above. But what do we actually mean by data driven decision making? Mastering (if possible) statistics, inference, and experimental design is part of a lifelong process of understanding and interpreting data to solve applied problems in business and the sciences. It's not just about conducting your own analysis and being your own worst critic, but also about interpreting, criticizing, translating and applying the work of others. Biologist and geneticist Kevin Folta put this well once in a Talking Biotech podcast:

"I've trained for 30 years to be able to understand statistics and experimental design and interpretation...I'll decide based on the quality of the data and the experimental design....that's what we do."

In 'Uncontrolled' Jim Manzi states:

"observing a naturally occurring event always leaves open the possibility of confounded causes...though in reality no experimenter can be absolutely certain that all causes have been held constant the conscious and rigorous attempt to do so is the crucial distinction between an experiment and an observation."

Statistical inference and experimental design provide us with a structured way to think about real world problems and the data we have to solve them while avoiding as much as possible the gut based data story telling that intentional or not, can sometimes be confounded and misleading. As Francis Bacon once stated:

"what is in observation loose and vague is in information deceptive and treacherous"

Statistics provides a rigorous way of thinking that moves us from mere observation to useful information.

*UPDATE: Kevin Gray wrote a very good article that really gets at the spirit of a lot of what I wanted to convey in this post.


See also:

To Explain or Predict

Applied Econometrics