# Research design causal relationship forecasting

### Causal model - Wikipedia

Causal (Multivariate) Forecasting Methods: Regression methods. Make projections of the future by modeling the causal relationship between a series and Qualitative methods include Delphi, market research, panel consensus, scenario. In a causal forecasting model, the forecast for the quantity of interest “rides There must be a relationship between values of the independent and Least Squares Fits The method of least squares is a formal procedure for curve fitting. A causal model is a conceptual model that describes the causal mechanisms of a system. Causal models can improve study designs by providing clear rules for Causal models are mathematical models representing causal relationships.

Their responses are collated and a copy is given to each of the participants. The participants are asked to comment on extreme views and to defend or modify their original opinion based on what the other participants have written.

Again, the answers are collated and fed back to the participants. In the final round, participants are asked to reassess their original opinion in view of those presented by other participants. The Delphi method general produces a rapid narrowing of opinions. It provides more accurate forecasts than group discussions.

Furthermore, a face-to-face discussion following the application of the Delphi method generally degrades accuracy. Simulation methods - Simulation methods involve using analogs to model complex systems. These analogs can take on several forms. A mechanical analog might be a wind tunnel for modeling aircraft performance. An equation to predict an economic measure would be a mathematical analog. A metaphorical analog could involve using the growth of a bacteria colony to describe human population growth.

Game analogs are used where the interactions of the players are symbolic of social interactions. Mathematical analogs are of particular importance to futures research. They have been extremely successful in many forecasting applications, especially in the physical sciences. In the social sciences however, their accuracy is somewhat diminished. The extraordinary complexity of social systems makes it difficult to include all the relevant factors in any model.

Clarke reminds us of a potential danger in our reliance on mathematical models. As he points out, these techniques often begin with an initial set of assumptions, and if these are incorrect, then the forecasts will reflect and amplify these errors. One of the most common mathematical analogs in societal growth is the S-curve. The model is based on the concept of the logistic or normal probability distribution.

All processes experience exponential growth and reach an upper asymptopic limit. Modis has hypothesized that chaos like states exist at the beginning and end of the S-curve. The disadvantage of this S-curve model is that it is difficult to know at any point in time where you currently are on the curve, or how close you are to the asymtopic limit. The advantage of the model is that it forces planners to take a long-term look at the future. Another common mathematical analog involves the use of multivariate statistical techniques.

These techniques are used to model complex systems involving relationships between two or more variables. Multiple regression analysis is the most common technique.

Unlike trend extrapolation models, which only look at the history of the variable being forecast, multiple regression models look at the relationship between the variable being forecast and two or more other variables. Multiple regression is the mathematical analog of a systems approach, and it has become the primary forecasting tool of economists and social scientists.

The object of multiple regression is to be able to understand how a group of variables working in unison affect another variable. The multiple regression problem of collinearity mirrors the practical problems of a systems approach. Paradoxically, strong correlations between predictor variables create unstable forecasts, where a slight change in one variable can have dramatic impact on another variable.

In a multiple regression and systems approach, as the relationships between the components of the system increase, our ability to predict any given component decreases. Gaming analogs are also important to futures research. Gaming involves the creation of an artificial environment or situation. Players either real people or computer players are asked to act out an assigned role. The "role" is essentially a set of rules that is used during interactions with other players.

While gaming has not yet been proven as a forecasting technique, it does serve two important functions.

## Forecasting

First, by the act of designing the game, researchers learn to define the parameters of the system they are studying. Second, it teaches researchers about the relationships between the components of the system. Cross-impact matrix method - Relationships often exist between events and developments that are not revealed by univariate forecasting techniques.

The cross-impact matrix method recognizes that the occurrence of an event can, in turn, effect the likelihoods of other events.

Probabilities are assigned to reflect the likelihood of an event in the presence and absence of other events. The resultant inter-correlational structure can be used to examine the relationships of the components to each other, and within the overall system. The advantage of this technique is that it forces forecasters and policy-makers to look at the relationships between system components, rather than viewing any variable as working independently of the others.

Scenario - The scenario is a narrative forecast that describes a potential course of events. Like the cross-impact matrix method, it recognizes the interrelationships of system components. The scenario describes the impact on the other components and the system as a whole. It is a "script" for defining the particulars of an uncertain future.

Scenarios consider events such as new technology, population shifts, and changing consumer preferences. Scenarios are written as long-term predictions of the future. A most likely scenario is usually written, along with at least one optimistic and one pessimistic scenario.

The primary purpose of a scenario is to provoke thinking of decision makers who can then posture themselves for the fulfillment of the scenario s. The three scenarios force decision makers to ask: Decision trees - Decision trees originally evolved as graphical devices to help illustrate the structural relationships between alternative choices.

As our understanding of feedback loops improved, decision trees became more complex. Their structure became the foundation of computer flow charts. Computer technology has made it possible create very complex decision trees consisting of many subsystems and feedback loops.

Decisions are no longer limited to dichotomies; they now involve assigning probabilities to the likelihood of any particular path. Decision theory is based on the concept that an expected value of a discrete variable can be calculated as the average value for that variable. The expected value is especially useful for decision makers because it represents the most likely value based on the probabilities of the distribution function.

The application of Bayes' theorem enables the modification of initial probability estimates, so the decision tree becomes refined as new evidence is introduced. Utility theory is often used in conjunction with decision theory to improve the decision making process. It recognizes that dollar amounts are not the only consideration in the decision process.

Other factors, such as risk, are also considered. Combining Forecasts It seems clear that no forecasting technique is appropriate for all situations. There is substantial evidence to demonstrate that combining individual forecasts produces gains in forecasting accuracy.

There is also evidence that adding quantitative forecasts to qualitative forecasts reduces accuracy. Research has not yet revealed the conditions or methods for the optimal combinations of forecasts. Judgmental forecasting usually involves combining forecasts from more than one source. Informed forecasting begins with a set of key assumptions and then uses a combination of historical data and expert opinions.

Involved forecasting seeks the opinions of all those directly affected by the forecast e. These techniques generally produce higher quality forecasts than can be attained from a single source.

Combining forecasts provides us with a way to compensate for deficiencies in a forecasting technique. By selecting complementary methods, the shortcomings of one technique can be offset by the advantages of another. Difficulties in Forecasting Technology Clarke describes our inability to forecast technological futures as a failure of nerve.

When a major technological breakthrough does occur, it takes conviction and courage to accept the implications of the finding. Even when the truth is starring us in the face, we often have difficulty accepting its implications. Clark refers to this resistance to change as cowardice, however, it may be much deeper. Cognitive dissonance theory in psychology has helped us understand that resistance to change is a natural human characteristic.

It is extremely difficult to venture beyond our latitudes of acceptance in forecasting new technologies. Clarke states that knowledge can sometimes clog the wheels of imagination. He embodied this belief in his self-proclaimed law: When he states that something is impossible, he is very probably wrong.

We generally perceive the existence of only one past. When two people give conflicting stories of the past, we tend to believe that one of them must be lying or mistaken. This widely accepted view of the past might not be correct. Historians often interject their own beliefs and biases when they write about the past.

Facts become distorted and altered over time. It may be that past is a reflection of our current conceptual reference. In the most extreme viewpoint, the concept of time itself comes into question. The future, on the other hand, is filled will uncertainty. Facts give way to opinions.

As de Jouvenel points out, the facts of the past provide the raw materials from which the mind makes estimates of the future. All forecasts are opinions of the future some more carefully formulated than others. The act of making a forecast is the expression of an opinion. The future, as described by de Jouvenel, consists of a range of possible future phenomena or events. These futuribles are those things that might happen.

Defining a Useful Forecast Science fiction novelist Frederik Pohl has suggested that the "only time a forecast has any real utility is when it is not totally reliable". He proposes a thought experiment where a Gypsy fortune teller predicts that we will be run over and killed when we leave the tea room.

If we know that the Gypsy's predictions are one hundred percent accurate, then Pohl states that the fortune is useless, because we would be unable to alter the forecast. In other words, predictions only become useful when they are not completely reliable.

The apparent paradox created by Pohl's thought experiment is only a function of the particular situation. The paradox exists only when 1 we want the future to be different than the prediction, and 2 when we believe that there is no way for us to adapt to or affect the forthcoming changes.

Pohl's thought experiment actually doesn't even meet that criteria, since one could present a convincing argument that it is more desirable to spend the rest of our lives confined to the comfort of a tea room than to leave and meet certain death.

Obviously, our new life would be difficult to accept and adapt to, but it could be done. Prisoners do it all the time. A forecast can be one hundred percent accurate and still be useful. For example, suppose our Gypsy had told us that after leaving her tea room we would safely return home. Again, since we know that her forecasts are completely accurate, we would receive emotional comfort from her predictions. In a more tangible example, suppose the prediction is that our manufacturing company will receive twice as many orders for widgets as we had anticipated.

### Time Series Analysis for Business Forecasting

Since the forecast is one hundred percent accurate, we would be wise to order more raw materials and increase our production staff to meet the coming demand. The goal of forecasting is to be as accurate as possible.

In the case of business demand forecasting, it is naive to suggest that an accurate forecast is useless. On the contrary, a more accurate forecast enables us to plan the use our resources in a more ecological fashion. We can minimize waste by adapting to our expectations of the future. It is sometimes useful in thought experiments to look at the situation from the opposite perspective. Suppose we know that our Gypsy is always wrong in her predictions. Her accuracy is guaranteed to be zero.

Note that this is different than random forecasts, where she might hit the mark once in a while. The Gypsy sighs with relief and says that there is no fatal accident in store for us today. According to Pohl's reasoning, this should provide the most useful forecast because it has the least accuracy. It's obvious, though, that this fortune is as useless as the one where she is completely accurate.

Leaving the Gypsy's tea-room is not something we would want to do. If we view accuracy as a continuum, it may be that the antonym of accuracy is randomness instead of inaccuracy. In this case, Pohl's theory would suggest that random forecasts are more useful than accurate forecasts.

In demand forecasting, the degree of over- and under-utilization of our resources is proportional to the difference between the observed and predicted values. Random forecasts are entirely unacceptable for this type of application. Pohl's thought experiment is very important because it forces us to look at the theoretical foundations of forecasting. First, Pohl's experiment may not be valid because it violates a basic assumption of forecasting i. Second, the usefulness of a forecast does not always seem to be related to its accuracy.

Both extremes completely accurate and completely inaccurate can produce useful or useless forecasts. The usefulness of a forecast is not something that lends itself readily to quantification along any specific dimension such as accuracy. It involves complex relationships between many things, including the type of information being forecast, our confidence in the accuracy of the forecast, the magnitude of our dissatisfaction with the forecast, and the versatility of ways that we can adapt to or modify the forecast.

In other words, the usefulness of a forecast is an application sensitive construct. Each forecasting situation must be evaluated individually regarding its usefulness. One of the first rules of doing research is to consider how the results will be used.

It is important to consider who the readers of the final report will be during the initial planning stages of a project. It is wasteful to expend resources on research that has little or no use.

The same rule applies to forecasting. We must strive to develop forecasts that are of maximum usefulness to planners. This means that each situation must be evaluated individually as to the methodology and type of forecasts that are most appropriate to the particular application.

Do Forecasts Create the Future A paradox exists in preparing a forecast. If a forecast results in an adaptive change, then the accuracy of the forecast might be modified by that change. Suppose the forecast is that our business will experience a ten percent drop in sales next month. We adapt by increasing our promotion effort to compensate for the predicted loss. This action, in turn, could affect our sales, thus changing the accuracy of the original forecast.

Many futurists de Jouvenel, Dublin, Pohl, and others have expressed the idea that the way we contemplate the future is an expression of our desire to create that future. Physicist Dennis Gabor, discoverer of holography, claimed that the future is invented, not predicted.

Introduction to causal models

The implication is that the future is an expression of our present thoughts. The idea that we create our own reality is not a new concept. It is easy to imagine how thoughts might translate into actions that affect the future. Biblical records speak of faith as the force that could move mountains. Recent research in quantum mechanics suggests that this may be more than just a philosophical concept.

At a quantum level, matter itself might simply be a manifestation of thought. Electrons and other subatomic particles seem to exist only when physicists are looking for them, otherwise, they exist only as energy.

An incredible discovery was made at the University of Paris in A team of researchers lead by Alain Aspect found that under certain conditions, electrons could instantaneously communicate with each other across long distances. The results of this experiment have been confirmed by many other researchers, although the implications are exceedingly hard to accept. Three explanations are possible: All three explanations rock our perception of reality. David Bohm has explained Aspect's experiment by hypothesizing a holographic universe in which reality is essentially a projection of some deeper dimension that we are not able to comprehend.

### Causal research - Wikipedia

Instantaneous communication is possible because the distance between the particles is an illusion. Neurophysiologist Karl Pribram has also theorized about the holographic nature of reality.

His theory is based on a study of the way that the brain recalls memory patterns, but the implications are the same. Reality is a phantasm.

If reality is an illusion, then the future is also an illusion. The phenomena of being able to see the future is known as precognition. Most people believe that to some degree they can predict the future. Fortune-tellers, however, believe they can view the future. There is a major difference. Besides, this method invariably fails to detect the weak signals that exist in data but remain unnoticed until somebody afterwards understands that it was these factors that finally determined the direction of the evolution.

Generally, you should always try to find out the rational explanation behind the statistical association that you are using as the basis of your forecast. It is always safer to forecast on the basis of a causal model described belowthan to forecast on the basis of statistical associations only. Applying a Causal Model A reliable method of prediction becomes possible if you have, through research, obtained a model which not only describes as in the previous section the development of the phenomenon to be predicted but also explains it, in other words enumerates the reasons why it happens.

In the best case the reasons and their outcomes are assembled as a model defining the dynamic invariance of change in the process to be predicted. The weather, for example, need today no more be predicted on the basis of a statistical association of air pressure and weather only.

The science of meteorology has lately advanced so much that we now know and can make use of the invariable structure of moving cyclones Fig. Even the proverb about red skies has now been given an explanation: When the storm eventually passes, the sky will clear in the western sky.

If sunset occurs simultaneously, the light will cast a red glow on the clouds above, now moving towards the east.

The most elementary method of forecasting on the basis of a causal model is to use the model just like a statistical associationexplained earlier. This is particularly easy when one of the variables in the model is time: If time is not included in the causal model, the model may still be helpful, because you can often predict the development of the independent variable easier than the future of the dependent variable or of the entire system - not least because of the fact that a reason normally precedes its result and it is thus not so distant in the future as the outcome will be.

When you know the causal relationships between the variables you will be able to use much more advanced methods of forecasting than mere statistical models would permit. You can better assess whether the model remains valid also in the future.

You can assess with sensitivity analysis the probable error of the forecast. You will also be able to modify the model, with a high degree of reliability, according to the requirements of the situation. If you want not only to forecast but also to change the future, you can pinpoint those changes in the independent variables that are needed to cause the desired change in the dependent variables.

The causal model is often so complicated that it is best managed by using a computer. Even then, you will usually need an illustrative presentation of your model to clarify your thinking and finally to be presented in the report. In such an illustration you will need a notation system to describe the various logical relations between the variables. The computer program will often be able to print out the model, using its in-built notations. If you can find no suitable ready made notation systems, you can devise one.

A legendary example of a large causal model was fabricated by the so-called Club of Rome in This model, published in the book The Limits to Growth, consists of dozens of variables, including the world population, birth rate, industrial and agricultural production, the non renewable resources, and pollution.

In the model, the levels, or physical quantities which can be measured directly, were indicated with rectanglesrates that influence those levels with valvesand auxiliary variables that influence the rate equations with circles. Time delays were indicated by sections within rectangles.

Real flows of people, goods, money, etc. Clouds represent sources or "sinks" exits of material that are not important to the model behaviour. The Club of Rome started building their "World Model" by first constructing five sub-models. These concentrated on the five "basic quantities": One of the sub-systems included the causal relations and feedback loops between population, capital, agriculture, and pollution fig.

Finally the researchers combined all the five sub-models and thus created the final World Model, part of which is illustrated below.

Examples of the forecasts produced by the Club of Rome can be seen later on. The examples above were from quantitative models; however the same principle can be applied when predicting on the basis of qualitative models which have explanatory power.

Examples of these are found in Explaining a Development. Note that although the causal type of explanation was discussed above and it is most often used for prediction, the function or motive types of explanation could also be used as a basis of a forecast. The models that are available for forecasting are usually simplified so that they contain only the most important factors that affect the phenomenon to be predicted.

Beside these, most empirical phenomena are influenced by a great number of minor factors, but their influence is so small that it disappears among errors or measurement and in the random fluctuation of the cardinal factors. Researchers often call these minor factors "noise" and simply disregard them. Nevertheless, you cannot always count on that the relationships expressed in your model remain constant during the whole time span of your forecast.

Sometimes it happens that a factor that until now has played only a marginal role will suddenly gain importance and will finally change the direction of the development.

Such factors which initially seem unimportant but finally become crucial are sometimes called weak signals. In order to identify such factors you can try various approaches, such as: Try to contemplate the context of the phenomenon in a wider perspective.

Consult other experts perhaps with the Delphi method and ask their opinion about the model that you are using. If your original model was made in cooperation with experts that have a long experience of the phenomenon to be predicted, it is possible that they have been accustomed to overlook a factor that nevertheless can be important quite soon.

Other people, less experienced with the phenomenon, may sometimes find new and surprising vantage points to look at the phenomenon. If you can find another system that has already undergone the development that you are trying to predict, you can use the analogy method and inspect whether the factors that have caused this development are present in the system to be predicted, albeit perhaps in an embryonic degree.

If your model is quantitative, you can try to find out which independent variables have had an gradually increasing influence on the phenomenon to be predicted. This can be done, for example, by calculating twice the correlation between such a factor and the phenomenon: Determining Limits It is often advantageous to use one method for the short time part of the forecast and another one for the long-term period.

For the near future linear extrapolation is often useful, while it often happens that common sense, research, or other source of general knowledge tells you that the evolution that you are forecasting is subject to pre-set limits or laws which dictate not the nearest events but rather a more distant future. You may, for example, be studying the growth of a plant knowing that the steady growth will eventually reach an end.

If that is the case, you may combine two forecasting methods: Typical examples of such long term developments are: The s-curve is usual if the growth has natural limits, as is the case with plants and with the natural resources of the earth, the "catastrophe curve" describes the end of a development which has had a smooth, gradual start but is nevertheless eventually expected to reach a complete extinction, perhaps an abrupt one.